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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...

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60,313 views • 11 days ago •via X (Twitter)

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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 balls churning in a spinning hexagon Outputs: Kimi K2.7 Code: $0.28 on 52.4k tokens GPT-5.5: $0.93 on 23.4k tokens Spring pendulums and blocks came out even. The balls Kimi did better: its pile spins with the drum when GPT's bounce around in pure chaos. On price to quality, K2.7 Code is the clear pick
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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 balls churning in a spinning hexagon Outputs: Kimi K2.7 Code: $0.28 on 52.4k tokens GPT-5.5: $0.93 on 23.4k tokens Spring pendulums and blocks came out even. The balls Kimi did better: its pile spins with the drum when GPT's bounce around in pure chaos. On price to quality, K2.7 Code is the clear pick

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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:

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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

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I was skeptical about giving GLM-5.2 the task to build a super mario snes style game in a single HTML file so i ran the test on Opus and also on GLM-5.2 Opus didnt even try to make a game or made something but it wasn't a game GLM-5.2 one shotted a pretty functional game.. WILD

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Frontier-level agentic capabilities and long-horizon coding, open source and fully private. That's GLM 5.2, now on Venice. Here's a full walkthrough to build a retro video game with GLM 5.2 and OpenCode, starting from a single prompt:

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I gave GLM-5.2 one goal: Turn a 2D dungeon crawler it built into a 2.5D isometric Diablo-style game with the visual fidelity of Diablo I. Then I let Z Code cook for 10+ hours. After a week of testing, I'd say its long-horizon execution is close to GPT-5.5 medium. The biggest difference is its agent behavior: GLM-5.2 is much more conservative, so it tends to take a longer path to the objective. Once you understand its quirks, it's remarkably capable. GPT-5.5 /goal also has its own quirks.

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GLM 5.2 just beat Fable 5 at website design. 🌐 The crazy part: GLM is text-only. It can build the site, but it can’t inspect the result. So we paired it with Browser Use v2 multimodal QA subagents. > Reviews the site and finds bugs > Judges the aesthetics > Sends GLM targeted fixes to iterate on Build + full QA cost less than $0.75. Try it now ↓

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Opus 4.8 vs GLM 5.2 everyone is praising Glm on my timeline, so i had to test it myself > both models were given the same prompt > GLM was super fast and used like 2% of my weekly limit > Opus took over 20 mins and used like 3% of weekly limits which one do you think won?

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45,180 views • 9 days ago

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🌘 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:

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GLM 4.7 is one of the strongest open-source coding models available—but most developers aren't prompting it correctly. We put together 10 rules to help you get the most out of it: - Front-load instructions (it has a strong recency bias) - Use firm language: "must" and "strictly" > soft suggestions - Break complex tasks into smaller steps - Disable reasoning for simple tasks, enable it for hard ones - Use critic agents for code review, QA, and validation - Pair it with a frontier model for the hardest 10% of workloads - and more… GLM 4.7 hits 96% on Tau² Bench and 86% on GPQA Diamond. At 1,500 tokens/sec on Cerebras, it's 20x faster than closed-source alternatives on GPUs.
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GLM 4.7 is one of the strongest open-source coding models available—but most developers aren't prompting it correctly. We put together 10 rules to help you get the most out of it: - Front-load instructions (it has a strong recency bias) - Use firm language: "must" and "strictly" > soft suggestions - Break complex tasks into smaller steps - Disable reasoning for simple tasks, enable it for hard ones - Use critic agents for code review, QA, and validation - Pair it with a frontier model for the hardest 10% of workloads - and more… GLM 4.7 hits 96% on Tau² Bench and 86% on GPQA Diamond. At 1,500 tokens/sec on Cerebras, it's 20x faster than closed-source alternatives on GPUs.

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633,658 views • 4 months ago

GLM-5.2 is legit. It produces games easily and as accurately as SOTA closed-source models. My GLM 5.2 versus Fable 5 comparison is close, but I prefer Fable 5 just a little. Both games are equal in nearly all respects, but Fable 5 produced slightly better graphics and more challenging gameplay. Both needed 2 shots to get all the prompt instructions correct.
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GLM-5.2 is legit. It produces games easily and as accurately as SOTA closed-source models. My GLM 5.2 versus Fable 5 comparison is close, but I prefer Fable 5 just a little. Both games are equal in nearly all respects, but Fable 5 produced slightly better graphics and more challenging gameplay. Both needed 2 shots to get all the prompt instructions correct.

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18,710 views • 9 days ago

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.
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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.

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Brian and Terry break down our 11th win of the season, and Brian talks with Coach May and three players on episode 348 of Defend the Block! Listen here »
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Brian and Terry break down our 11th win of the season, and Brian talks with Coach May and three players on episode 348 of Defend the Block! Listen here »

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Introducing GLM 5.2 for autoresearch GLM 5.2 is the first open weights model we've tried on our autoresearch pipeline that's proven capable for real research tasks. With Fable 5's restrictions on research, having an open weights alternative is a huge win for open source Watch it carry out fully async vs colocated sync RL training on Harbor code contests across two 8xH100 nodes on top of SkyRL. Resolves setup issues, tracks runs to completion, and produces a full comparison of throughput and reward stability
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Introducing GLM 5.2 for autoresearch GLM 5.2 is the first open weights model we've tried on our autoresearch pipeline that's proven capable for real research tasks. With Fable 5's restrictions on research, having an open weights alternative is a huge win for open source Watch it carry out fully async vs colocated sync RL training on Harbor code contests across two 8xH100 nodes on top of SkyRL. Resolves setup issues, tracks runs to completion, and produces a full comparison of throughput and reward stability

alphaXiv

266,039 views • 6 days ago