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New Kimi K2.7 Code performs at GPT-5.5 level 3x cheaper! We gave both models the same three prompts: build a self-contained HTML5 canvas sim with real physics, no libraries. A spring pendulum on a stretching coil, a 1 kg block trading collisions with a 100,000 kg block, and 22... show more

atomic.chat

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132,678 просмотров • 11 дней назад •via X (Twitter)

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New Z.ai GLM-5.2 beats Kimi K2.7 Code on physics contest! We gave both models the same three prompts and asked them to build self contained HTML5 sims with real physics and no libraries: 1. Pool break 2. Block on a bed of springs 3. Galton board Outputs: GLM-5.2: 12,640 tokens Kimi K2.7 Code: 7,420 tokens GLM 5.2 nailed all three, and it did it with way more detail and polish. The break conserved momentum, the block bounced off the springs and the Galton beads spread into a clean bell curve. Kimi struggled on every scene: its block fell straight through the springs, its break didn't look realistic with the balls colliding all wrong, and on the Galton board its balls overlapped and piled into each other instead of spreading out

New Z.ai GLM-5.2 beats Kimi K2.7 Code on physics contest! We gave both models the same three prompts and asked them to build self contained HTML5 sims with real physics and no libraries: 1. Pool break 2. Block on a bed of springs 3. Galton board Outputs: GLM-5.2: 12,640 tokens Kimi K2.7 Code: 7,420 tokens GLM 5.2 nailed all three, and it did it with way more detail and polish. The break conserved momentum, the block bounced off the springs and the Galton beads spread into a clean bell curve. Kimi struggled on every scene: its block fell straight through the springs, its break didn't look realistic with the balls colliding all wrong, and on the Galton board its balls overlapped and piled into each other instead of spreading out

atomic.chat

59,742 просмотров • 7 дней назад

New Fable 5 beats Opus 4.8 on real world physics simulations We gave both models the same three prompts and asked them to build self contained HTML5 sims with real physics and no libraries: 1. Chaotic double pendulum 2. Galton board 3. Water in a spinning drum (WCSPH) Generation cost Fable 5: $3.35 on 68.7k tokens, time 14m 47s Opus 4.8: $0.93 on 38.9k tokens, time 8m 10s Fable clearly did better on the water simulation, producing a much more solid and continuous body of water. Opus left larger gaps near the walls, scattered particles around the scene, and struggled to keep the fluid stable.

New Fable 5 beats Opus 4.8 on real world physics simulations We gave both models the same three prompts and asked them to build self contained HTML5 sims with real physics and no libraries: 1. Chaotic double pendulum 2. Galton board 3. Water in a spinning drum (WCSPH) Generation cost Fable 5: $3.35 on 68.7k tokens, time 14m 47s Opus 4.8: $0.93 on 38.9k tokens, time 8m 10s Fable clearly did better on the water simulation, producing a much more solid and continuous body of water. Opus left larger gaps near the walls, scattered particles around the scene, and struggled to keep the fluid stable.

atomic.chat

1,583,267 просмотров • 14 дней назад

🌘 Meet Kimi K2.7 Code HighSpeed! A high-speed mode of our latest open-source multimodal coding model, Kimi K2.7 Code. ⚡️ Up to 6× faster: Around 180 tok/s on coding tasks with median-length inputs, and up to 260 tok/s on shorter-context tasks. 🔷 Rolling out to Kimi Code Beta Program members, Kimi API developers, and Kimi Business users. (Access will remain limited for now due to capacity constraints.) 🔷 No invite needed. Anyone who joins the Beta Program has a chance to get access 👉 Open intelligence should be instant, affordable, and borderless. We'll continue improving the model and expanding access as more capacity becomes available! 🔗 Kimi Code: 🔗 API:

🌘 Meet Kimi K2.7 Code HighSpeed! A high-speed mode of our latest open-source multimodal coding model, Kimi K2.7 Code. ⚡️ Up to 6× faster: Around 180 tok/s on coding tasks with median-length inputs, and up to 260 tok/s on shorter-context tasks. 🔷 Rolling out to Kimi Code Beta Program members, Kimi API developers, and Kimi Business users. (Access will remain limited for now due to capacity constraints.) 🔷 No invite needed. Anyone who joins the Beta Program has a chance to get access 👉 Open intelligence should be instant, affordable, and borderless. We'll continue improving the model and expanding access as more capacity becomes available! 🔗 Kimi Code: 🔗 API:

Kimi.ai

513,240 просмотров • 8 дней назад

Opus 4.8 for planning 🤝 GPT 5.5 for implementation Claude is a nicer conversation and /grilling partner than Codex in my experience, but GPT 5.5 on low cranks out higher quality code in fewer tokens. Here's how I divide up the work:

Opus 4.8 for planning 🤝 GPT 5.5 for implementation Claude is a nicer conversation and /grilling partner than Codex in my experience, but GPT 5.5 on low cranks out higher quality code in fewer tokens. Here's how I divide up the work:

Ben Holmes

23,010 просмотров • 20 дней назад

New Google Gemma 4 12B claims near-26B performance - we tested both! We ran both models locally on one RTX 4090 and gave each the same task: write a self-contained HTML5 canvas animation with real physics in one file without libraries. Three scenes - a Galton board, two blocks colliding off a wall, and a chaotic triple pendulum Outputs: Gemma 4 26B-A4B: 15 GB VRAM usage, 6.9k tokens, 138 tok/s Gemma 4 12B: 9 GB VRAM usage, 8.9k tokens, 80 tok/s Same Gemma 4 family, but the 26B-A4B won every scene and ran ~1.7x faster - on just 4B active params. The 12B stayed very close though, on almost half the VRAM - which makes it the ideal model for a 16 GB laptop

New Google Gemma 4 12B claims near-26B performance - we tested both! We ran both models locally on one RTX 4090 and gave each the same task: write a self-contained HTML5 canvas animation with real physics in one file without libraries. Three scenes - a Galton board, two blocks colliding off a wall, and a chaotic triple pendulum Outputs: Gemma 4 26B-A4B: 15 GB VRAM usage, 6.9k tokens, 138 tok/s Gemma 4 12B: 9 GB VRAM usage, 8.9k tokens, 80 tok/s Same Gemma 4 family, but the 26B-A4B won every scene and ran ~1.7x faster - on just 4B active params. The 12B stayed very close though, on almost half the VRAM - which makes it the ideal model for a 16 GB laptop

atomic.chat

150,374 просмотров • 20 дней назад

everyone is talking about the new kimi k2.7 code so i tested it for real. i built a fruit ninja game i play with my index finger. webcam tracks my finger, turns it into a blade, i swipe and slice fruit midair. real tracking in the browser. and yes it's open source

everyone is talking about the new kimi k2.7 code so i tested it for real. i built a fruit ninja game i play with my index finger. webcam tracks my finger, turns it into a blade, i swipe and slice fruit midair. real tracking in the browser. and yes it's open source

Farhan

29,728 просмотров • 10 дней назад

MiniMax M3 vs GLM-5.2 vs Kimi K2.7 in Lunar Lander contest! 🔥 Last test of the day before going back to learning & build phase with Local Models, Swift and Apple Core AI 💪 I took the opportunity to test the MiniMax Code app for macOS. Video below speeded up. Followed by Lunar Lander implementation from the various models. My ranking: 🥇 GLM 5.2 🥈 MiniMax M3 🥉 Kimi K2.7 Coding Prompt and final results in this gist:

MiniMax M3 vs GLM-5.2 vs Kimi K2.7 in Lunar Lander contest! 🔥 Last test of the day before going back to learning & build phase with Local Models, Swift and Apple Core AI 💪 I took the opportunity to test the MiniMax Code app for macOS. Video below speeded up. Followed by Lunar Lander implementation from the various models. My ranking: 🥇 GLM 5.2 🥈 MiniMax M3 🥉 Kimi K2.7 Coding Prompt and final results in this gist:

Ivan Fioravanti ᯅ

21,604 просмотров • 9 дней назад

saw someone say kimi k2.7 is good for frontend now, so i ran the same prompts through kimi, codex and claude opus and clicked through what they built.

saw someone say kimi k2.7 is good for frontend now, so i ran the same prompts through kimi, codex and claude opus and clicked through what they built.

Dom

122,617 просмотров • 7 дней назад

We’ve been building this in secret for a while. We love Claude Code, and we’ve reimplemented “Claude Code” in Genspark. Introducing Genspark AI Developer - “Claude Code” in the browser - “Claude Code” on mobile - Everyone can build, no coding experience required - Supports multiple frontier models: Claude Sonnet 4, Claude Opus 4.1, GPT‑5, Kimi + Groq 1/4

We’ve been building this in secret for a while. We love Claude Code, and we’ve reimplemented “Claude Code” in Genspark. Introducing Genspark AI Developer - “Claude Code” in the browser - “Claude Code” on mobile - Everyone can build, no coding experience required - Supports multiple frontier models: Claude Sonnet 4, Claude Opus 4.1, GPT‑5, Kimi + Groq 1/4

Eric Jing

23,245,701 просмотров • 10 месяцев назад

GLM 5.2 vs Kimi K2.7 both were tested on same settings with same prompt > GLM one shotted everything in 35 mins > Kimi needed extra prompts to fix movements and took 30 mins super surpised by how good GLM 5.2 is, while being so cheap which one do you think won?

GLM 5.2 vs Kimi K2.7 both were tested on same settings with same prompt > GLM one shotted everything in 35 mins > Kimi needed extra prompts to fix movements and took 30 mins super surpised by how good GLM 5.2 is, while being so cheap which one do you think won?

J A Z I I

63,064 просмотров • 10 дней назад

🚨 Kimi Cooked So Hard with this release This is one Shotted by Kimi k2.7 fast , math guys try kimi i think its better in math too now Fable was able to do this but kimi only took less than a min lol , i had early access to it

🚨 Kimi Cooked So Hard with this release This is one Shotted by Kimi k2.7 fast , math guys try kimi i think its better in math too now Fable was able to do this but kimi only took less than a min lol , i had early access to it

Chetaslua

55,400 просмотров • 8 дней назад

lol...Kimi really cooked Claude Design tested the same prompt on both Kimi K2.6 and Claude Design, and these are the outputs not to mention Kimi is 7x cheaper and 100% open source... see prompt in the comments👇

lol...Kimi really cooked Claude Design tested the same prompt on both Kimi K2.6 and Claude Design, and these are the outputs not to mention Kimi is 7x cheaper and 100% open source... see prompt in the comments👇

Farhan

323,403 просмотров • 1 месяц назад

Introducing Codespace A virtual computer that runs custom Claude Code on server. It is using 2 models: Kimi K2 + Claude Sonnet 4 in a sync. I achieved same code quality with 2x better reasoning and 53% less model cost. Access it today CodeGuide (500 credits for everyone)

Introducing Codespace A virtual computer that runs custom Claude Code on server. It is using 2 models: Kimi K2 + Claude Sonnet 4 in a sync. I achieved same code quality with 2x better reasoning and 53% less model cost. Access it today CodeGuide (500 credits for everyone)

CJ Zafir

31,070 просмотров • 10 месяцев назад

Kimi K2.7 Code just dropped from Moonshot AI. GLM 5.2 is faster, smarter, and cheaper. I purchased both the $39 and $99 plans to test it. I zapped my usage in 30 minutes. The horror game didn't work The Minecraft clone was unplayable The model got stuck outputting thousands of hashtags instead of code The Remotion video looked like a PowerPoint 262K context window in 2026 is embarrassing No BridgeMind stamp of approval. Full review video below

Kimi K2.7 Code just dropped from Moonshot AI. GLM 5.2 is faster, smarter, and cheaper. I purchased both the $39 and $99 plans to test it. I zapped my usage in 30 minutes. The horror game didn't work The Minecraft clone was unplayable The model got stuck outputting thousands of hashtags instead of code The Remotion video looked like a PowerPoint 262K context window in 2026 is embarrassing No BridgeMind stamp of approval. Full review video below

BridgeMind

46,548 просмотров • 8 дней назад

Kimi K2.7-Code is now available on AI/ML API! Moonshot's latest is built for long-horizon agentic coding that self-corrects instead of one-shotting. So we gave it a hard one. SpaceX is going public, a company built on one habit: fly, fail, fix, fly again. That loop carried Falcon to the droneship and Starship toward orbit. We wanted to see if a model could run that same loop on its own. We gave four Kimi agents a 2D flight-physics sim they can't modify and one goal: reach orbit and land the booster on a droneship. Flight 1 broke up at max-Q. Flight 2 cleared max-Q but staged too early and fell back short of orbit. Flight 3 made orbit, then missed the droneship and hit the water. Flight 4 fixed the landing math and put the booster on the ship. Four flights, each failure feeding the next fix. The interesting result isn't the rocket, it's the closed-loop debugging. Architecture in the comments. Got any ideas for droneship name?

Kimi K2.7-Code is now available on AI/ML API! Moonshot's latest is built for long-horizon agentic coding that self-corrects instead of one-shotting. So we gave it a hard one. SpaceX is going public, a company built on one habit: fly, fail, fix, fly again. That loop carried Falcon to the droneship and Starship toward orbit. We wanted to see if a model could run that same loop on its own. We gave four Kimi agents a 2D flight-physics sim they can't modify and one goal: reach orbit and land the booster on a droneship. Flight 1 broke up at max-Q. Flight 2 cleared max-Q but staged too early and fell back short of orbit. Flight 3 made orbit, then missed the droneship and hit the water. Flight 4 fixed the landing math and put the booster on the ship. Four flights, each failure feeding the next fix. The interesting result isn't the rocket, it's the closed-loop debugging. Architecture in the comments. Got any ideas for droneship name?

AI/ML API

22,015 просмотров • 11 дней назад

Kimi Code is good at video reasoning with Kimi K2.6.🎬 Drag in reference videos, ask about colors, shots, or visual style, and Kimi Code can generate a ready-to-use .cube LUT file.🎞️

Kimi Code is good at video reasoning with Kimi K2.6.🎬 Drag in reference videos, ask about colors, shots, or visual style, and Kimi Code can generate a ready-to-use .cube LUT file.🎞️

Kimi Developers

13,574 просмотров • 15 дней назад

At Perplexity, GPT-5.5 in Codex helped build an internal tool in under an hour. In Perplexity Computer workflows, GPT-5.5 used 56% fewer tokens on the same complex tasks, creating faster feedback loops for users.

At Perplexity, GPT-5.5 in Codex helped build an internal tool in under an hour. In Perplexity Computer workflows, GPT-5.5 used 56% fewer tokens on the same complex tasks, creating faster feedback loops for users.

OpenAI Developers

68,320 просмотров • 2 месяцев назад

GPT-5.6 vs GPT-5.5 on my custom spaceship prompt. I gave both models the exact same custom prompt. This is also the same prompt I previously gave to Fable 5. For context, GPT-5.6 Pro worked for 87 minutes, while GPT-5.5 Extra High worked for 34 minutes and 42 seconds. As I’ve said before, based on great authority GPT-5.6 will be an incremental/soldi improvement over GPT-5.5, not a “Fable killer.” My rough expectation has been that it would trade blows with Fable 5 on some benchmarks, maybe win around half depending on the category, but not clearly surpass it overall. And again fable five will have bigger model smell, but this was expected. After testing this coding output, that view feels pretty accurate. GPT-5.6 is clearly better than GPT-5.5 in several visual areas. The lighting, shading, chairs, object details, and exterior of the spaceship looked noticeably stronger. The scene was also easier to test. I do want to give GPT-5.5 credit though. It built out the rooms much much better and the planets looked better than GPT-5.6’s. It was also interesting that both GPT-5.5 and GPT-5.6 produced better-looking planets than Fable 5 in this specific test. The downside with GPT-5.5 was stability. The game was much glitchier and harder to test compared to GPT-5.6. But when it comes to the core of the demo, which is the spaceship itself, Fable 5 still beat both models pretty comfortably. GPT-5.6 is impressive, but from this test, it looks exactly like what I expected which was a meaningful incremental improvement over GPT-5.5, at least for indie game demos, but not something that replaces Fable 5. In collaboration with Chetaslua

GPT-5.6 vs GPT-5.5 on my custom spaceship prompt. I gave both models the exact same custom prompt. This is also the same prompt I previously gave to Fable 5. For context, GPT-5.6 Pro worked for 87 minutes, while GPT-5.5 Extra High worked for 34 minutes and 42 seconds. As I’ve said before, based on great authority GPT-5.6 will be an incremental/soldi improvement over GPT-5.5, not a “Fable killer.” My rough expectation has been that it would trade blows with Fable 5 on some benchmarks, maybe win around half depending on the category, but not clearly surpass it overall. And again fable five will have bigger model smell, but this was expected. After testing this coding output, that view feels pretty accurate. GPT-5.6 is clearly better than GPT-5.5 in several visual areas. The lighting, shading, chairs, object details, and exterior of the spaceship looked noticeably stronger. The scene was also easier to test. I do want to give GPT-5.5 credit though. It built out the rooms much much better and the planets looked better than GPT-5.6’s. It was also interesting that both GPT-5.5 and GPT-5.6 produced better-looking planets than Fable 5 in this specific test. The downside with GPT-5.5 was stability. The game was much glitchier and harder to test compared to GPT-5.6. But when it comes to the core of the demo, which is the spaceship itself, Fable 5 still beat both models pretty comfortably. GPT-5.6 is impressive, but from this test, it looks exactly like what I expected which was a meaningful incremental improvement over GPT-5.5, at least for indie game demos, but not something that replaces Fable 5. In collaboration with Chetaslua

Chris

187,652 просмотров • 5 дней назад

Used kimi to vibe code a full website from a rough sketch I drew in 5 minutes. It came out better than stuff i've spent days on. Bro what is even the point of me anymore.

Used kimi to vibe code a full website from a rough sketch I drew in 5 minutes. It came out better than stuff i've spent days on. Bro what is even the point of me anymore.

Kasif

23,149 просмотров • 1 месяц назад

Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!

Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!

Avi Chawla

108,824 просмотров • 1 месяц назад