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Compare AI models side by side with the Artificial Analysis model comparison tool Select any model and compare them across: ➤ Artificial Analysis Intelligence Index scores ➤ Individual benchmark scores ➤ Artificial Analysis Capability Index scores including Finance & Accounting, Strategy & Ops, Legal, Healthcare & Medical, Engineering, and... show more

Artificial Analysis

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26,511 görüntüleme • 5 gün önce •via X (Twitter)

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Theo - t3.gg profil fotoğrafı

Theo - t3.gg5 gün önce

I’ll give you guys the codebase for this if you promise to actually ship it with dark mode and all the useless models turned off by default (WTF is step3 and why is it on???)

大伟｜AI × Web3 profil fotoğrafı

大伟｜AI × Web35 gün önce

十周陪伴比单个高光更有价值：观众真正买单的不是完美人设，而是看得见的成长轨迹。能让本人也为变化自豪，这段内容才完成了从流量到认同的转化。

Louis Amira profil fotoğrafı

Louis Amira5 gün önce

Why is the model list incomplete? @TheUnbiasedCo Pareto would help a lot of people get more intelligence for less money.

🇮🇷اشکانیان🇮🇷 profil fotoğrafı

🇮🇷اشکانیان🇮🇷5 gün önce

Plaese test fledge alpha

Raimo profil fotoğrafı

Raimo5 gün önce

add classification models!! Jev Vs openAi Decisions Vs others

Demi🧜🏽‍♀️ profil fotoğrafı

Demi🧜🏽‍♀️5 gün önce

Compare AI models side by side with the Artificial Analysis model comparison tool. You and @JonLeeMiller are the two accounts I most enjoy following.

Jatin Garg profil fotoğrafı

Jatin Garg5 gün önce

the comparison is only useful if it shows how models behave differently on the same exact prompt. aggregate benchmark scores don't predict which one works better for your specific use case.

ShadowAguy profil fotoğrafı

ShadowAguy5 gün önce

the spreadsheet will outlive the leaderboard, which is how benchmark charts quietly become historical artifacts between model releases.

0x7z7z profil fotoğrafı

0x7z7z5 gün önce

Dark mode guys for Gods sake

The AI Maximalist profil fotoğrafı

The AI Maximalist5 gün önce

Artificial Analysis gives you clear scores for every model side by side so the best choice stands out without guesswork and saves time when picking tools

Benzer Videolar

Qwen3.8-27B is now live at Cerebras speed. The dense, open-weight model from Qwen scores 34 on the Artificial Analysis Intelligence Index—making it comparable to models such as GPT-5.6 Luna, DeepseekV4 Pro, and Claude Sonnet 4.6.

Qwen3.8-27B is now live at Cerebras speed. The dense, open-weight model from Qwen scores 34 on the Artificial Analysis Intelligence Index—making it comparable to models such as GPT-5.6 Luna, DeepseekV4 Pro, and Claude Sonnet 4.6.

Cerebras

292,623 görüntüleme • 27 gün önce

We’re launching Optima. Now anyone can create a custom benchmark for their use case, leveraging Artificial Analysis’ leading research and platform Building and running benchmarks is difficult. We have distilled Artificial Analysis’ research and experience developing benchmarks into Optima, a new platform for benchmarking models on your own workloads and comparing performance, speed and cost efficiency. Optima allows you to find the best model for your task, or an equally performant alternative to your current setup at 10x lower cost or time per task. We’ve integrated Artificial Analysis' research and experience in benchmarks across the Optima workflow: ➤ Build benchmarks based on your own data and use cases: There are three ways to build a benchmark with Optima. Upload an existing evaluation dataset from your own files or Hugging Face, or import agent traces from platforms including Arize AI, Braintrust and langfuse.com. Install the Optima skill to build a benchmark using context from your coding environment and previous sessions. Or simply describe your use case and provide example inputs and outputs, and Optima will build the benchmark for you ➤ Run across the latest models: Run the same benchmark across leading models in a single click, and keep your leaderboard up to date as soon as new models are released ➤ Bring Artificial Analysis grading to your own benchmark: Evaluate responses against objective rubric criteria or using the same pairwise judging approach used for Artificial Analysis benchmarks including GDPval-AA and AA-Briefcase. For pairwise judging, select your preferred responses from a sample and Optima uses those preferences to rank models across your test set ➤ Compare performance, cost and time efficiency: Optima measures more than model performance. Cost per Task and Time per Task are tracked alongside benchmark scores, with category-level results and support for custom metrics, allowing you to compare the tradeoffs between models for your specific use case Ahead of launch, here are examples questions our beta testers answered with Optima: ➤ Which model can save me 10x the cost without a meaningful decrease in quality for my finance & accounting agent? ➤ Which model best matches the writing style of lawyers for my legal agent? ➤ Which model can best identify different elements in my custom image dataset? Optima is available today. Build your own benchmark at

We’re launching Optima. Now anyone can create a custom benchmark for their use case, leveraging Artificial Analysis’ leading research and platform Building and running benchmarks is difficult. We have distilled Artificial Analysis’ research and experience developing benchmarks into Optima, a new platform for benchmarking models on your own workloads and comparing performance, speed and cost efficiency. Optima allows you to find the best model for your task, or an equally performant alternative to your current setup at 10x lower cost or time per task. We’ve integrated Artificial Analysis' research and experience in benchmarks across the Optima workflow: ➤ Build benchmarks based on your own data and use cases: There are three ways to build a benchmark with Optima. Upload an existing evaluation dataset from your own files or Hugging Face, or import agent traces from platforms including Arize AI, Braintrust and langfuse.com. Install the Optima skill to build a benchmark using context from your coding environment and previous sessions. Or simply describe your use case and provide example inputs and outputs, and Optima will build the benchmark for you ➤ Run across the latest models: Run the same benchmark across leading models in a single click, and keep your leaderboard up to date as soon as new models are released ➤ Bring Artificial Analysis grading to your own benchmark: Evaluate responses against objective rubric criteria or using the same pairwise judging approach used for Artificial Analysis benchmarks including GDPval-AA and AA-Briefcase. For pairwise judging, select your preferred responses from a sample and Optima uses those preferences to rank models across your test set ➤ Compare performance, cost and time efficiency: Optima measures more than model performance. Cost per Task and Time per Task are tracked alongside benchmark scores, with category-level results and support for custom metrics, allowing you to compare the tradeoffs between models for your specific use case Ahead of launch, here are examples questions our beta testers answered with Optima: ➤ Which model can save me 10x the cost without a meaningful decrease in quality for my finance & accounting agent? ➤ Which model best matches the writing style of lawyers for my legal agent? ➤ Which model can best identify different elements in my custom image dataset? Optima is available today. Build your own benchmark at

Artificial Analysis

133,508 görüntüleme • 1 ay önce

It's official: Qwen3.8-27B just scored 52 on the Artificial Analysis Intelligence Index. We now have an open-weight model that matches GPT-5.6 Luna (max) AND can run locally... even in your browser with custom WebGPU kernels! What a time to be alive! 🤯

It's official: Qwen3.8-27B just scored 52 on the Artificial Analysis Intelligence Index. We now have an open-weight model that matches GPT-5.6 Luna (max) AND can run locally... even in your browser with custom WebGPU kernels! What a time to be alive! 🤯

Xenova

187,874 görüntüleme • 1 ay önce

$🇺🇸 Grok jumped five points in five weeks: From 56 to 61 on the Artificial Analysis Intelligence Index. They beat competitors on knowledge work, crushed it on Harvey LAB by 13 points, and the kicker? “Grok 4.6 is a Sol-quality model… at a fraction of the price. $6 vs $30.” xAI is closing the gap AND making frontier intelligence cheaper and faster than the field expected. Writer: Val$

🇺🇸 Grok jumped five points in five weeks: From 56 to 61 on the Artificial Analysis Intelligence Index. They beat competitors on knowledge work, crushed it on Harvey LAB by 13 points, and the kicker? “Grok 4.6 is a Sol-quality model… at a fraction of the price. $6 vs $30.” xAI is closing the gap AND making frontier intelligence cheaper and faster than the field expected. Writer: Val

Mario Nawfal

38,577 görüntüleme • 1 ay önce

Kash Patel says he has transformed the FBI into an agency now dominated by artificial intelligence. According to Patel, AI is being deployed across critical systems, including fingerprint analysis and the National Threat Operations Center. "I'm using it everywhere." "AI was never used at the FBI until we got there."

Kash Patel says he has transformed the FBI into an agency now dominated by artificial intelligence. According to Patel, AI is being deployed across critical systems, including fingerprint analysis and the National Threat Operations Center. "I'm using it everywhere." "AI was never used at the FBI until we got there."

Shadow of Ezra

162,947 görüntüleme • 5 ay önce

Gemini 3.8 Live and 3.8 Live Extended Thinking are here. Following 3.5 Transcribe last month, this continues our focus on real-time voice agents. 🐸🐸 - 82.6 (#1) on Artificial Analysis Quality Index - $0.005/min input and $0.018/min output. - 35.1 (#1) Agentic task completion (τ-banking) 3.8 Live Extended Thinking can think in the background and run asynchronously tool calls while keep talking to you. It uses background thinking to coordinate multi-step tool use without losing conversational flow. Available in Google AI Studio Gemini API, Google Search, and Google Gemini or as partner plugins in LiveKit, Pipecat AI, @LangChainAI, and Vercel

Gemini 3.8 Live and 3.8 Live Extended Thinking are here. Following 3.5 Transcribe last month, this continues our focus on real-time voice agents. 🐸🐸 - 82.6 (#1) on Artificial Analysis Quality Index - $0.005/min input and $0.018/min output. - 35.1 (#1) Agentic task completion (τ-banking) 3.8 Live Extended Thinking can think in the background and run asynchronously tool calls while keep talking to you. It uses background thinking to coordinate multi-step tool use without losing conversational flow. Available in Google AI Studio Gemini API, Google Search, and Google Gemini or as partner plugins in LiveKit, Pipecat AI, @LangChainAI, and Vercel

Philipp Schmid

10,751 görüntüleme • 23 gün önce

$SITUATION EXPLAINED: How did Grok catch up this fast? • Grok 4.5 scored 56 on the Artificial Analysis index five weeks ago. Grok 4.6 scores 61, one behind Fable, at the same price • SOTA on knowledge work and legal, beating Fable on GDPval and Sol on Harvey LAB by 13 points Anastasios Nikolas Angelopoulos: "Grok 4.6 is a Sol-quality model, at a fraction of the price. Grok $6 vs Sol $30" sof 𓋹: "Now that the frontier labs can't release anything because, ooh, scary, close to critical capability thresholds, forget Chinese companies, all of the American companies are racing to close the gap."$

SITUATION EXPLAINED: How did Grok catch up this fast? • Grok 4.5 scored 56 on the Artificial Analysis index five weeks ago. Grok 4.6 scores 61, one behind Fable, at the same price • SOTA on knowledge work and legal, beating Fable on GDPval and Sol on Harvey LAB by 13 points Anastasios Nikolas Angelopoulos: "Grok 4.6 is a Sol-quality model, at a fraction of the price. Grok $6 vs Sol $30" sof 𓋹: "Now that the frontier labs can't release anything because, ooh, scary, close to critical capability thresholds, forget Chinese companies, all of the American companies are racing to close the gap."

MTS

34,203 görüntüleme • 1 ay önce

From a scrappy side project built to solve their own LLM optimization problems to becoming the industry’s de-facto independent scoreboard, Micah Hill-Smith and George Cameron went through the arc of launching Artificial Analysis for free, paying benchmarking costs out of pocket, and growing it into what many now call the “new Gartner of AI” for enterprises, labs, and developers. We sat down with Micah and George to unpack why truly independent benchmarking is so hard (prompt variance, eval saturation, mystery-shopper policies), how the Artificial Analysis Intelligence Index evolved as old benchmarks broke, and what new metrics actually matter now such as agentic evals (GDPVal-AA). We also dig into the economics behind the “smile curve” of AI: why intelligence is getting 100–1000× cheaper per unit while total spend explodes, how reasoning and agents change token efficiency, and their bet that evals must continuously evolve or risk training the industry to optimize for the wrong things. swyx Micah Hill-Smith George Cameron

From a scrappy side project built to solve their own LLM optimization problems to becoming the industry’s de-facto independent scoreboard, Micah Hill-Smith and George Cameron went through the arc of launching Artificial Analysis for free, paying benchmarking costs out of pocket, and growing it into what many now call the “new Gartner of AI” for enterprises, labs, and developers. We sat down with Micah and George to unpack why truly independent benchmarking is so hard (prompt variance, eval saturation, mystery-shopper policies), how the Artificial Analysis Intelligence Index evolved as old benchmarks broke, and what new metrics actually matter now such as agentic evals (GDPVal-AA). We also dig into the economics behind the “smile curve” of AI: why intelligence is getting 100–1000× cheaper per unit while total spend explodes, how reasoning and agents change token efficiency, and their bet that evals must continuously evolve or risk training the industry to optimize for the wrong things. swyx Micah Hill-Smith George Cameron

Latent.Space

33,451 görüntüleme • 9 ay önce

Gemini 3.5 Flash is a step forward for Google on speed and agentic capabilities but comes at a trade-off of being higher cost than prior models We have measured up to ~280 output tokens/sec, placing it on the speed/intelligence Pareto frontier and well ahead of Gemini 3 Flash. It also shows a major uplift on agentic tasks, reaching ~1650 ELO on GDPVal-AA. The trade-off: cost is up ~5x versus Gemini 3 Flash, driven by higher token prices (3x higher than Gemini 3 Flash) and higher token usage. In this video, Declan Jackson, Member of Technical Staff at Artificial Analysis, breaks it down.

Gemini 3.5 Flash is a step forward for Google on speed and agentic capabilities but comes at a trade-off of being higher cost than prior models We have measured up to ~280 output tokens/sec, placing it on the speed/intelligence Pareto frontier and well ahead of Gemini 3 Flash. It also shows a major uplift on agentic tasks, reaching ~1650 ELO on GDPVal-AA. The trade-off: cost is up ~5x versus Gemini 3 Flash, driven by higher token prices (3x higher than Gemini 3 Flash) and higher token usage. In this video, Declan Jackson, Member of Technical Staff at Artificial Analysis, breaks it down.

Artificial Analysis

16,055 görüntüleme • 4 ay önce

Announcing Artificial Analysis Video Arena - the first crowdsourced comparison for Text to Video models Text to Video models are accelerating rapidly and crossing quality thresholds every month. We created Video Arena to compare them using the only source of truth for visual media - human preference! Video Arena includes hundreds of videos from the leading video models, including: - Runway's Runway Gen 3 Alpha - Pika's Pika 1.5 - Luma's Dream Machine - MiniMax / Hailuo AI (MiniMax) - Kling AI's Kling 1.0 - Zhipu AI's CogVideoX-5B Voting is open now and we’ll be announcing the first leaderboard results within 24 hours. Any predictions? In the meantime, you can see your own ‘personal leaderboard’ of how you’ve ranked the video models after 30 votes. Link to the Artificial Analysis Video Arena in the below tweet! 👇

Announcing Artificial Analysis Video Arena - the first crowdsourced comparison for Text to Video models Text to Video models are accelerating rapidly and crossing quality thresholds every month. We created Video Arena to compare them using the only source of truth for visual media - human preference! Video Arena includes hundreds of videos from the leading video models, including: - Runway's Runway Gen 3 Alpha - Pika's Pika 1.5 - Luma's Dream Machine - MiniMax / Hailuo AI (MiniMax) - Kling AI's Kling 1.0 - Zhipu AI's CogVideoX-5B Voting is open now and we’ll be announcing the first leaderboard results within 24 hours. Any predictions? In the meantime, you can see your own ‘personal leaderboard’ of how you’ve ranked the video models after 30 votes. Link to the Artificial Analysis Video Arena in the below tweet! 👇

Artificial Analysis

27,108 görüntüleme • 1 yıl önce

Testing four AI video models from the same input image and prompt. The scene: two cybernetic soldiers dueling with energy swords in a rainy neon cyberpunk city. This thread breaks down scene richness, physics, and camera motion for each model. First, a side-by-side comparison video of PixVerse V6 and Seedance 2.0.

Testing four AI video models from the same input image and prompt. The scene: two cybernetic soldiers dueling with energy swords in a rainy neon cyberpunk city. This thread breaks down scene richness, physics, and camera motion for each model. First, a side-by-side comparison video of PixVerse V6 and Seedance 2.0.

Jibril

113,346 görüntüleme • 5 ay önce

Eval scores in 2026 are broken. MMLU at 91%+, GSM8K at 94%+, yet models still can't handle basic multi-step tasks. And reported scores don't even agree across model cards, papers, and platforms. We just shipped Community Evals on Hugging Face: - Benchmark datasets now host live leaderboards (MMLU-Pro, GPQA, HLE) - Scores live in model repos as versioned YAML - Anyone can submit evals to any model via PR without merging. - Verified badges for reproducible runs via Inspect AI This won't fix saturation or stop test set contamination. But it makes the game visible. What was evaluated, how, when, and by whom. Done trusting black-box leaderboards. Time to decentralize evals.

Eval scores in 2026 are broken. MMLU at 91%+, GSM8K at 94%+, yet models still can't handle basic multi-step tasks. And reported scores don't even agree across model cards, papers, and platforms. We just shipped Community Evals on Hugging Face: - Benchmark datasets now host live leaderboards (MMLU-Pro, GPQA, HLE) - Scores live in model repos as versioned YAML - Anyone can submit evals to any model via PR without merging. - Verified badges for reproducible runs via Inspect AI This won't fix saturation or stop test set contamination. But it makes the game visible. What was evaluated, how, when, and by whom. Done trusting black-box leaderboards. Time to decentralize evals.

Ben Burtenshaw

19,259 görüntüleme • 8 ay önce

🚀 New update for the Grafilab AI ecosystem! We’ve just updated new frontier models in our Playground and Unified API, including the highly anticipated models by DeepSeek and Qwen families! 🧠⚡ But that’s not all… you can now run Side-by-Side Model Comparisons directly in the Grafilab Playground! 🥊 Compare outputs from Gemini, DeepSeek, Qwen, and more in real-time, on the exact same prompt, with zero code. Find the perfect model for your workload instantly. Try it out now:

🚀 New update for the Grafilab AI ecosystem! We’ve just updated new frontier models in our Playground and Unified API, including the highly anticipated models by DeepSeek and Qwen families! 🧠⚡ But that’s not all… you can now run Side-by-Side Model Comparisons directly in the Grafilab Playground! 🥊 Compare outputs from Gemini, DeepSeek, Qwen, and more in real-time, on the exact same prompt, with zero code. Find the perfect model for your workload instantly. Try it out now:

Grafilab - Unleash AI

805,542 görüntüleme • 3 ay önce

Been giving my M3 Ultra Studio some love and tinkering with MLX. Started the afternoon at 42 tok/s. Now: 73 tok/s, and there’s still more on the table. Running Qwen3.8-Flash-Next @ 4-bit locally. Now for the crazy bit: Artificial Analysis Intelligence Index v4.3: GPT-5.6 Sol 39 (Medium) GPT-5.6 Sol 42 (High) Qwen3.8-Flash-Next 42 GPT-5.6 Sol 44 (XHigh) GPT-5.6 Sol 47 (Max) Grok 4.5 39 (High) Claude Sonnet 5 38 (Max) *Qwen score currently estimated by Artificial Analysis. A 4-bit open-weights model running at 73 tok/s on a Mac under my desk... with an estimated intelligence score matching GPT-5.6 Sol at High reasoning. Local AI is getting ridiculous. 🔥 Plus, this model does not say no to a bit of red teaming. Once I am happy with it, I will do a pr for Jun Kim's oMLX :)

Been giving my M3 Ultra Studio some love and tinkering with MLX. Started the afternoon at 42 tok/s. Now: 73 tok/s, and there’s still more on the table. Running Qwen3.8-Flash-Next @ 4-bit locally. Now for the crazy bit: Artificial Analysis Intelligence Index v4.3: GPT-5.6 Sol 39 (Medium) GPT-5.6 Sol 42 (High) Qwen3.8-Flash-Next 42 GPT-5.6 Sol 44 (XHigh) GPT-5.6 Sol 47 (Max) Grok 4.5 39 (High) Claude Sonnet 5 38 (Max) *Qwen score currently estimated by Artificial Analysis. A 4-bit open-weights model running at 73 tok/s on a Mac under my desk... with an estimated intelligence score matching GPT-5.6 Sol at High reasoning. Local AI is getting ridiculous. 🔥 Plus, this model does not say no to a bit of red teaming. Once I am happy with it, I will do a pr for Jun Kim's oMLX :)

Ash Hart

18,827 görüntüleme • 1 ay önce

Qwen3.6-35B-A3B-NVFP4 Benchmark: MoE vs. Dense Performance Report This report details a performance comparisonbetween two artificial intelligence models, focusing on the Qwen3.6-35B-A3B-NVFP4 and its predecessor. While both models achieved flawless accuracy across a comprehensive 65-test benchmark covering vision, video, and reasoning, the 35B version demonstrated significant speed advantages. Utilizing a Mixture of Experts architecture, the 35B model achieved nearly four times higher throughput and drastically reduced latency for initial responses. It also showed superior efficiency in speculative decoding and successfully managed massive context lengths up to 128,000 tokens. Ultimately, the source recommends the 35B model for high-speed production environments, while noting the 27B model remains more stable for specific complex reasoning modes.

Qwen3.6-35B-A3B-NVFP4 Benchmark: MoE vs. Dense Performance Report This report details a performance comparisonbetween two artificial intelligence models, focusing on the Qwen3.6-35B-A3B-NVFP4 and its predecessor. While both models achieved flawless accuracy across a comprehensive 65-test benchmark covering vision, video, and reasoning, the 35B version demonstrated significant speed advantages. Utilizing a Mixture of Experts architecture, the 35B model achieved nearly four times higher throughput and drastically reduced latency for initial responses. It also showed superior efficiency in speculative decoding and successfully managed massive context lengths up to 128,000 tokens. Ultimately, the source recommends the 35B model for high-speed production environments, while noting the 27B model remains more stable for specific complex reasoning modes.

Mike Gannotti

10,457 görüntüleme • 3 ay önce

AI safety is back in the spotlight, driven by concerns about recursive self improvement. We built the first third party benchmark to measure just how close AI is building its own successor. In collaboration with marimo and CoreWeave we built the The RSI Index, which runs every frontier model through the same AI research tasks and scores each one against the strongest published results. So far, the models can do the work, but is far from the human frontier.

AI safety is back in the spotlight, driven by concerns about recursive self improvement. We built the first third party benchmark to measure just how close AI is building its own successor. In collaboration with marimo and CoreWeave we built the The RSI Index, which runs every frontier model through the same AI research tasks and scores each one against the strongest published results. So far, the models can do the work, but is far from the human frontier.

Vals AI

26,618 görüntüleme • 27 gün önce

Grok 4.5 and Muse Spark 1.1 are now available on the AI/ML platform for testing. What a week; all 3 models in the comparison dropped just recently. There will be a lot to experiment with. AI/ML has tested the new models on prompts for playable mini-games, including a Fruit Ninja-style slicer and a Crossy Road clone. All three are live in the Playground and via API for side-by-side runs on output and cost.

Grok 4.5 and Muse Spark 1.1 are now available on the AI/ML platform for testing. What a week; all 3 models in the comparison dropped just recently. There will be a lot to experiment with. AI/ML has tested the new models on prompts for playable mini-games, including a Fruit Ninja-style slicer and a Crossy Road clone. All three are live in the Playground and via API for side-by-side runs on output and cost.

🚨 AI News | TestingCatalog

37,973 görüntüleme • 3 ay önce

Introducing AI Lab Monetization Play. AI Lab Monetization Play tracks the revenue run-rate of major AI laboratories in real time using public and alternative data signals. The Play provides a continuously updated view of commercial scale across the frontier AI landscape. ARR Dashboard The dashboard estimates annual recurring revenue for Anthropic, OpenAI, and other frontier labs. Each estimate is broken down by revenue stream, growth trajectory, and implied valuation multiple. It surfaces which labs are scaling fastest and where the commercial momentum is concentrated. Model Routing and Market Share The Play tracks real-time model usage share across inference providers. It measures routing volume through aggregators to identify which models are capturing production workloads. Shifts in routing patterns signal adoption inflections before they appear in reported financials. Model Pricing and Capabilities The Play maintains a comprehensive pricing table across all major foundation models. Input and output token costs, context windows, and capability tiers are mapped side by side. It serves as the reference layer for competitive positioning and margin structure across the model ecosystem. Available to all paid clients on our full-stack research platform. Start here:

Introducing AI Lab Monetization Play. AI Lab Monetization Play tracks the revenue run-rate of major AI laboratories in real time using public and alternative data signals. The Play provides a continuously updated view of commercial scale across the frontier AI landscape. ARR Dashboard The dashboard estimates annual recurring revenue for Anthropic, OpenAI, and other frontier labs. Each estimate is broken down by revenue stream, growth trajectory, and implied valuation multiple. It surfaces which labs are scaling fastest and where the commercial momentum is concentrated. Model Routing and Market Share The Play tracks real-time model usage share across inference providers. It measures routing volume through aggregators to identify which models are capturing production workloads. Shifts in routing patterns signal adoption inflections before they appear in reported financials. Model Pricing and Capabilities The Play maintains a comprehensive pricing table across all major foundation models. Input and output token costs, context windows, and capability tiers are mapped side by side. It serves as the reference layer for competitive positioning and margin structure across the model ecosystem. Available to all paid clients on our full-stack research platform. Start here:

FUNDA

37,874 görüntüleme • 2 ay önce

Our most intelligent workhorse model yet for coding and agents has arrived ⚡ Meet Gemini 3.7 Flash. — Crush that seemingly endless to-do list. Gemini Spark in the Google Gemini now uses 3.7 Flash. The new model can equip your personal AI agent to work even smarter for you by seamlessly handling complex, multi-step tasks across your Google Workspace apps like Gmail, Google Calendar and Google Docs — Enjoy a smoother build experience. The model thinks more diligently, putting more effort into multi-step planning and tool calls. A more disciplined execution means less manual oversight and fewer retries across engineering workflows — Build more, spend less. 3.7 Flash is available through the end of the year with an introductory price of half the original 3.6 Flash cost per million tokens ($0.75/1M input tokens and $3.75/1M output tokens)

Our most intelligent workhorse model yet for coding and agents has arrived ⚡ Meet Gemini 3.7 Flash. — Crush that seemingly endless to-do list. Gemini Spark in the Google Gemini now uses 3.7 Flash. The new model can equip your personal AI agent to work even smarter for you by seamlessly handling complex, multi-step tasks across your Google Workspace apps like Gmail, Google Calendar and Google Docs — Enjoy a smoother build experience. The model thinks more diligently, putting more effort into multi-step planning and tool calls. A more disciplined execution means less manual oversight and fewer retries across engineering workflows — Build more, spend less. 3.7 Flash is available through the end of the year with an introductory price of half the original 3.6 Flash cost per million tokens ($0.75/1M input tokens and $3.75/1M output tokens)

Google AI

129,191 görüntüleme • 1 ay önce