GLM / Z.ai (Zhipu AI)
A Tier · 8.0/10
**GLM 5.2 has a named production deployment at Perplexity: on 2026-09-21 Perplexity disclosed that Perplexity Computer sessions are served by GLM 5.2, post-trained by Perplexity on real user sessions** (21.2% relative cut in live tool-call failures). **GLM-5.3 and GLM-5.3-Flash released 2026-08-25** (weights on Hugging Face): 5.3 is a post-training-only upgrade of the 5.2 base that Z.ai calls 'the most capable open-weights model for coding' (Terminal Bench 2.1 88.2, DeepSWE 66.9, CyberGym 84.5) but moves from MIT to a custom licence; 5.3-Flash (320B/18B active, multimodal) stays MIT at $0.15/$0.50 per 1M.
Score Breakdown
Benchmark Scores
Benchmarks for GLM-5.2 (~753B MoE, launched 2026-06-13) -- vendor-published; third-party verification still settling
| Benchmark | Description | Score | |
|---|---|---|---|
| SWE-Bench Pro | 62.1% | ||
| GPQA Diamond | Graduate-level science questions | 91.2% | |
| AIME 2026 | 99.2% | ||
| Humanity's Last Exam (reasoning) | 40.5% | ||
| MMLU-Pro | Harder multi-subject reasoning | 81.2% | |
| HumanEval | Python code generation | 89.1% |
Last updated: 2026-06-18
Personality & Tone
The Z.ai research model
Tone: Academic and structured. GLM-4.6's instruction-tuned chat tends toward outlined, bullet-heavy responses and leans on established phrasing rather than casual voice.
Quirks: Strong on multilingual and tool use, weaker at playful conversation. Smaller community fine-tuning ecosystem than Llama or Qwen, so fewer 'flavored' checkpoints to pick from -- most deployments run the base instruction-tune.
The Good and the Bad
What we like
- +Named production deployment at scale: Perplexity disclosed (2026-09-21) that Perplexity Computer sessions are served by GLM 5.2 post-trained on real user sessions -- the clearest public evidence that the MIT-licensed 5.2 base is good enough to run a flagship Western consumer agent
- +GLM-5.1 (2026-04-07) topped SWE-Bench Pro at 58.4 -- beating GPT-5.4, Claude Opus 4.6, and every other open-weight model on that benchmark. The result is externally verified and is the strongest agentic-coding signal from any Chinese open-weight model in 2026
- +First frontier model trained entirely on 100,000 Huawei Ascend 910B chips with zero Nvidia in the training stack -- a genuine proof point that non-Nvidia training pipelines can reach frontier quality, with big implications for US-China compute strategy
- +GLM-5.2 and the new GLM-5.3-Flash are true MIT -- among the few frontier-tier open weights with zero commercial restrictions (note the GLM-5.3 flagship moved to a custom licence on 2026-08-25)
- +GLM-5.1 is SOTA among open models for agentic tool-use and function calling; GLM-4.6V is #1 open-source on MMBench, MathVista, OCRBench among multimodal models
- +200K context window handles long documents reliably. Strong Chinese + English performance (unlike DeepSeek which is English-biased)
What could be better
- −Smaller Western community than Qwen or DeepSeek -- fewer tutorials, quants, fine-tunes
- −English tone is noticeably more stilted than Claude or Mistral for creative writing
- −PRC content filters apply to politically sensitive topics
- −Ollama support lags behind Qwen/Llama/Mistral release cycles
Pricing
Self-hosted (Free)
- ✓Licence now depends on the model: GLM-5.2 and GLM-5.3-Flash are MIT (no MAU clauses); the GLM-5.3 flagship ships under a custom 'glm-5.3' licence -- read it before commercial deployment
- ✓Full weights on Hugging Face
- ✓Commercial use fully permitted on the MIT models
API (Z.ai / OpenRouter, GLM-5.2)
- ✓GLM-5.2 (launched 2026-06-13): ~753B MoE, 1M context, IndexShare architecture
- ✓GLM-5.1 (2026-04-07): 744B MoE / 40B active, $0.60 in / $2.20 out (still available)
- ✓GLM-4.6V (vision): tiered
- ✓VentureBeat: ~1/6 the cost of GPT-5.5 on long-horizon coding
System Requirements
Hardware needed to self-host. Min = smallest viable setup (usually heavy quantization). Max = full-precision / production-grade.
| Model variant | Min | Max |
|---|---|---|
| GLM-5.2 (~753B MoE, launched 2026-06-13, 1M context)MIT license. IndexShare sparse attention lowers per-token FLOPs ~2.9x at 1M context. Active-parameter count not published in the model card | 256 GB RAM + 48 GB GPU (Q2 offload; community quants landing post-launch) | 8x H100 FP8 or 4x H200 |
| GLM-5.1 (744B MoE / 40B active, launched 2026-04-07)MIT license. Trained entirely on Huawei Ascend 910B, not Nvidia | 256 GB RAM + 48 GB GPU (Q2 offload, production builds still landing) | 8× H100 FP8 or 4× H200 |
| GLM-4.6 (355B MoE, legacy)MIT license -- zero commercial restrictions | 128 GB RAM + 24 GB GPU (Q3 offload) | 4× H100 FP8 |
| GLM-4.6V (multimodal)Vision tower adds ~4 GB on top of base footprint | 128 GB RAM + 28 GB GPU (Q3 + vision tower) | 4× H100 FP8 |
| GLM-4-9B (small) | 6 GB VRAM (Q4) | 24 GB VRAM FP16 |
Known Issues
- NAMED PRODUCTION DEPLOYMENT -- PERPLEXITY COMPUTER RUNS ON GLM 5.2, POST-TRAINED BY PERPLEXITY (disclosed 2026-09-21, third-party vendor-primary; recorded here 2026-09-28): Perplexity's research post 'Learning from real-world experience' describes post-training 'a Perplexity Computer model' with rejection-sampling fine-tuning plus hint-guided self-distillation on real sessions, and discloses that Computer sessions are served by **GLM 5.2** with the trained checkpoint offered as a model option in Computer; in live use the later checkpoint reduced tool-call failures by 21.2% relative (2.24% to 1.77%, statistically significant) with no significant change in user dissatisfaction. Why it matters for this page: it is the first time a major Western consumer AI product has publicly named a Z.ai model as the engine behind a flagship agent, and it validates the MIT-licensed 5.2 base (not the custom-licensed 5.3) as the version enterprises can post-train and ship. Perplexity's Portable Computer, by contrast, runs Qwen 3.8 27B or Perplexity's own PPLX 27B locally (9/24), not GLM.Source: Perplexity (perplexity.ai/hub/blog/learning-from-real-world-experience, hub index date 'Sep 21, 2026') -- fetched 2026-09-28 via curl with browser UA; see perplexity.ts and perplexity-computer.ts · 2026-09-21
- STALENESS CATCH-UP -- GLM-5.3 AND GLM-5.3-FLASH SHIPPED 2026-08-25, AND THE FLAGSHIP'S LICENCE CHANGED FROM MIT TO A CUSTOM ONE (vendor-primary via Z.ai's Hugging Face model cards and API pricing page; this page was last reviewed 2026-07-09 and still describes GLM-5.2 as current): **(1) GLM-5.3** -- 'uses the same base model as GLM-5.2 -- every gain comes from post-training.' Z.ai's claims: 'the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench', open-source SOTA on Terminal Bench 3.0 and Agents' Last Exam, and -- stated unusually plainly -- '**Emergent Cyber Capability**: As we scaled post-training, cyber capability developed faster than we expected. GLM-5.3 is state of the art on CyberGym for vulnerability discovery, and its gains are largest further up the exploitation chain, where it more than doubles GLM-5.2 on exploitation benchmarks.' **Vendor benchmark table (GLM-5.3 / GLM-5.2 / Kimi K3 / DeepSeek-V4-Pro-0813 / Qwen3.8-Max / Opus 4.8 / Fable 5 / GPT-5.6 Sol):** Terminal Bench 2.1 **88.2** / 81.0 / 88.3 / 87.9 / 86.6 / 85.0 / 88.0 / 88.8; Terminal Bench 3.0 **28.3** / 4.6 / 17.4 / - / - / 21.1 / 33.7 / 34.6; DeepSWE v1.1 **66.9** / 46.2 / 67.5 / 62.7 / 56.6 / 58.0 / 69.7 / 72.7; CyberGym **84.5** (highest in the table) / 77.2 / 80.0 / 83.3 / 78.5 / 78.1 / 83.8 / 83.6; ExploitBench 54.4 / 24.4 / 32.2 / - / 28.8 / 40.0 / 78.0 / 76.5; Toolathlon Verified 73.0; AutomationBench **48.2** (highest); HLE w/ tools 62.5; GDPval-AA v2 **1769** (highest, evaluated by Artificial Analysis). So on Z.ai's own numbers 5.3 is at parity with Kimi K3 and DeepSeek V4-Pro among open weights and still 3-6 points behind Fable 5 / Sol on the hardest agentic suites, while leading everyone on CyberGym and GDPval. **LICENCE: GLM-5.3 ships under a custom 'glm-5.3' licence, not MIT** -- a change from GLM-5.2's MIT and the reason the 'MIT licensed' claim in this page's tagline no longer applies to the flagship. **API price $1.40 input / $4.40 output per 1M, cache read $0.26**, same as 5.2 (limited-time free cache write). **(2) GLM-5.3-Flash** -- 'the first natively multimodal model in the GLM-5 series', **320B total / 18B active**, a newly trained base with a **hybrid sparse + linear attention** architecture and Manifold-Constrained Hyper-Connections, 30T-token multimodal corpus; Z.ai says it 'outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks.' **MIT licensed. API $0.15 / $0.50 per 1M, cache read $0.03.** `reasoning_effort` low/high/max (defaults to max). **THE CYBER DISCLOSURE DESERVES A SECOND LOOK:** Z.ai is openly publishing exploitation-chain benchmark gains on a downloadable model, three weeks after OpenAI's 'Defender's Window' post warned of an open-weight cyber model 'a few months behind the frontier' -- GLM-5.3's ExploitBench 54.4 is well below Fable 5's 78.0 and Sol's 76.5, so it is not frontier-level, but it is the strongest open-weight cyber result any vendor has published. **Also 8/25:** BF16 builds of both. **Why this was missed:** z.ai/blog now 404s to plain fetches; the Hugging Face zai-org index by createdAt is the reliable enumeration path.Source: Hugging Face model cards zai-org/GLM-5.3 (created 2026-08-25, license glm-5.3, full benchmark table) and zai-org/GLM-5.3-Flash (created 2026-08-25, license mit); Z.ai API pricing (docs.z.ai/guides/overview/pricing: GLM-5.3 $1.4 / $0.26 cache / $4.4; GLM-5.3-Flash $0.15 / $0.03 / $0.50) -- all fetched 2026-09-14 · 2026-08-25
- ZCODE HARNESS LAUNCHED (2026-07-02, press-verified): Zhipu released **ZCode**, an agentic-coding harness for GLM-5.2 that lets developers build autonomous coding assistants on the model -- an explicit shot at Anthropic's Claude Code, leaning on developer frustration with Anthropic's recent access restrictions and Zhipu's open-weights positioning. Launch incentives: **5 million free tokens for new ZCode users** and a 50% data-quota boost for existing subscribers. Open-source status implied but not explicitly confirmed -- verify the repo/license before building on it. Strategically this completes the GLM-5.2 story: MIT-licensed frontier weights + a first-party harness = a full Claude Code substitute stack from one Chinese labSource: SCMP (scmp.com/tech/tech-trends/article/3359170, 2026-07-02) · 2026-07-02
- INDEPENDENT BENCHMARK PUBLISHED (verified 2026-07-04): Artificial Analysis now has a citable independent eval for GLM-5.2 -- **Intelligence Index = 51, ranked #1 of 93 open-weight models** (artificialanalysis.ai/models/glm-5-2), corroborating the vendor's 'tops the open-weights index' claim with a third-party number. Keep the Zhipu-reported figures (SWE-Bench Pro 62.1%, Terminal-Bench 2.1 81.0%) labeled as vendor benchmarks -- the AA-51 is the independently-measured one to lead with.Source: Artificial Analysis (artificialanalysis.ai/models/glm-5-2), Crypto Briefing · 2026-07-04
- MODEL LAUNCH (2026-06-13): **GLM-5.2** -- Z.ai's new open-weights flagship 'for long-horizon tasks', a substantial leap over GLM-5.1. ~753B-parameter MoE, MIT licensed, with a 1M-token context that sustains long-horizon work and the new **IndexShare** sparse-attention architecture that cuts per-token FLOPs ~2.9x at 1M-token context. Vendor-published benchmarks (third-party verification still settling): SWE-Bench Pro 62.1, GPQA-Diamond ~91.2, AIME 2026 ~99.2, HLE reasoning ~40.5. It tops the Artificial Analysis open-weights Intelligence Index, and VentureBeat reports it beats GPT-5.5 on several long-horizon coding benchmarks at roughly 1/6 the cost. MIT weights shipped to Hugging Face (zai-org/GLM-5.2) and ModelScope; works as a drop-in coding backend for Claude Code, Cline, and OpenCode. Note: exact active-parameter count for 5.2 was not published in the model card -- treat the MoE-active figure as unconfirmed pending a vendor spec sheet.Source: Hugging Face zai-org/GLM-5.2 model card, VentureBeat (z-ais-open-weights-glm-5-2-beats-gpt-5-5...), z.ai/blog/glm-5.2 · 2026-06-13
- GLM-4.6 requires specific tokenizer and chat template -- several community llama.cpp quants initially had broken tool-use until fixes landedSource: Hugging Face discussions, GitHub issues · 2026-03
- Refuses discussion of Tiananmen, Taiwan, Xi Jinping -- same PRC content filters as DeepSeek and QwenSource: Reddit r/LocalLLaMA · 2026-02
Best for
Teams that need frontier-class open weights for agentic coding: GLM-5.3-Flash (MIT, 320B/18B active, multimodal) for clean licensing at low cost, GLM-5.3 (custom licence) when you want the strongest open coding scores. Also strong for vision (GLM-4.6V).
Not for
Consumer-facing English content generation (Mistral or Claude write better), or ultra-low-resource deployment (use Gemma 4 or Phi-4 instead).
Our Verdict
Update 2026-09-14: GLM-5.3 (Aug 25) improves on 5.2 through post-training alone -- parity with Kimi K3 and DeepSeek V4-Pro on Z.ai's own table and the top open-weight CyberGym score -- but it drops MIT for a custom licence; GLM-5.3-Flash (320B/18B, multimodal, MIT, $0.15/$0.50) is now the clean-licence pick. Original verdict: With GLM-5.2 (June 13, 2026), Z.ai has the strongest MIT-licensed open-weights model of 2026 -- a ~753B MoE with a 1M context, SWE-Bench Pro 62.1, and the top spot on Artificial Analysis's open-weights index, which VentureBeat says edges GPT-5.5 on long-horizon coding at a fraction of the cost. The true MIT license puts it ahead of Llama 4 on licensing, and the agentic tool-use performance beats most of its open-weight peers. GLM-4.6V is legitimately the best open multimodal model on several benchmarks. The weakness is purely ecosystem: fewer Western fine-tunes and less Ollama coverage. If you're building an agent or multimodal product and want clean licensing, GLM is the pick.
Sources
- Perplexity: Learning from real-world experience (2026-09-21) -- Computer sessions served by GLM 5.2, post-trained checkpoint, 21.2% fewer tool-call failures (accessed 2026-09-28)
- Hugging Face: zai-org/GLM-5.3 model card -- post-training upgrade of the 5.2 base, benchmark table, custom GLM-5.3 licence (created 2026-08-25) (accessed 2026-09-14)
- Hugging Face: zai-org/GLM-5.3-Flash model card -- 320B/18B active, natively multimodal, MIT (created 2026-08-25) (accessed 2026-09-14)
- Z.ai API pricing -- GLM-5.3 $1.40/$4.40, GLM-5.3-Flash $0.15/$0.50 per 1M (verified 2026-09-14) (accessed 2026-09-14)
- Artificial Analysis: GLM-5.2 (Intelligence Index 51, #1 open-weight) (accessed 2026-07-04)
- Hugging Face: zai-org/GLM-5.2 model card (specs, license, benchmarks) (accessed 2026-06-18)
- VentureBeat: Z.ai's open-weights GLM-5.2 beats GPT-5.5 on long-horizon coding for ~1/6 the cost (accessed 2026-06-18)
- Winbuzzer: Z.ai releases GLM-5.1 754B tops SWE-Bench Pro (accessed 2026-04-17)
- TestingCatalog: Zhipu AI launches GLM-5.1 open-source model for coding (accessed 2026-04-17)
- Z.ai blog: GLM-4.6 and GLM-4.6V (accessed 2026-04-17)
- Hugging Face THUDM collection (accessed 2026-04-17)
- Artificial Analysis open-weights leaderboard (accessed 2026-04-17)
- OpenRouter pricing (accessed 2026-04-17)
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Alternatives to GLM / Z.ai (Zhipu AI)
Llama 4 (Meta)
Meta's open-weights family -- Scout (10M context), Maverick (multimodal 400B MoE). NOTE: Meta's frontier work moved to the proprietary Muse Spark line in April 2026; Llama remains downloadable and supported but is effectively in maintenance mode
Mistral AI
**New 2026-10-15: Timestral, Mistral's first time-series forecasting family (5M, 28M, 123M and 209M parameters, weights on Hugging Face since 10/14 under a non-Apache licence), with the 209M model claimed first on GIFT-Eval, TIME and fev-bench and CPU inference in 10-140 ms.** **Mistral Large 4 ('le Chonk', ML4) entered public preview on 2026-10-06: a 1T-parameter natively multimodal MoE with roughly 50B active parameters, a 1M context window, callable today as mistral-large-4 at $1.36 / $0.14 cached / $4.18 per 1M tokens (batch at half price), open weights promised by the end of October, and vendor-claimed open-weight leads in cyber (Cybench 93%), coding (DeepSWE v1.1 61.7%) and agentic work (AutomationBench 59.9%).** **Mistral opened a Munich hub for Physics AI and Industrial AI on 2026-09-28**, confirming the May 2026 acquisition of Emmi AI (30+ physicists and engineers in computational fluid dynamics, structural mechanics and multi-physics simulation), naming BMW (crash simulation) and Siemens Energy as partners, and committing to **build one gigawatt of European compute capacity by 2030**. **Mistral now powers Firefox Smart Window (2026-09-16)** -- Mozilla's beta AI browsing assistant runs on Mistral models for France and North America, UK and Germany later this year, with zero data retention. **EUR 3B Series D at a EUR 21B+ post-money (9/08, Samsung-led)**; Cloudera partnership (9/10); Medium 3 retired 8/31 in favour of Medium 3.5.
DeepSeek
**DeepSeek-V4.1-Flash-1017 (2026-10-17) replaced V4.1-Flash on the web, app and API at unchanged prices** (the deepseek-flash model name now serves it; MIT weights on Hugging Face), with higher scores and shorter outputs, and DeepSeek opened its in-house **Search and Fetch APIs at $0.75 per 1,000 calls per endpoint** -- server-side Search/Fetch calls through DeepSeek Harness, Claude Code and Codex stay free until 2026-10-31 16:00 UTC. Earlier: **DeepSeek-V4.1-Flash shipped 2026-09-10**: a 552B MoE on a new Causal Encoder-Decoder architecture (8B active for input, 16B for output), natively multimodal, MIT-licensed on HF, and priced BELOW the V4-Flash it replaces -- $0.15/$0.60 off-peak, $0.30/$1.20 peak per 1M, with cache-hit input cut to $0.003/$0.006. V4-Flash and V4-Flash-Vision-Exp are retired (aliases route to V4.1-Flash). DeepSeek said V4-Pro would be phased out on 9/14, then **reversed that four days later** -- V4-Pro stays on the API at unchanged $0.66/$1.98 off-peak, $1.32/$3.96 peak until V4.1-Pro lands. V4.1-Flash's vendor benchmarks beat V4-Pro (DeepSWE 74.2, Terminal-Bench 2.1 90.6, GPQA 90.9)
Gemma 4 (Google)
Google DeepMind's open-weights model family -- multimodal, 256K context, runs on edge devices **EmbeddingGemma 2 (announced 2026-10-06; weights on Hugging Face since 9/14) adds a 740M-parameter Apache 2.0 embedding model built on Gemma 4 that maps text, code, images, video and audio into one 768-dimensional space, with an 8K context, Matryoshka truncation down to 128 dimensions and about 191 MB of RAM for text-only use on a Pixel 11 Pro.**
DiffusionGemma (Google)
Google DeepMind's experimental open-weights TEXT-DIFFUSION model (June 10, 2026) -- 26B MoE (3.8B active), Apache 2.0, generates 256-token blocks in parallel with bidirectional attention for up to 4x faster output (1,000+ tok/s on H100). Trades some quality vs Gemma 4 for raw speed
Qwen (Alibaba)
**Qwen-Image-2.1 went open-weight (weights 2026-09-14, blog 2026-09-20)** -- a unified text-to-image and editing model with a 7B visual-generation component, native transparent (RGBA) output, up to 10 reference images and mask-guided local edits -- but under a **research-only licence, not Apache**: commercial use needs a separate licence from Alibaba. Alibaba's open-weights + API family, and August 2026 was its biggest open-release month yet: **Qwen3.8-2.4T-A95B -- the Max-class flagship -- went open-weight on 2026-08-08** (custom Qwen3.8-Max licence), **Qwen3.8-27B** dense landed Apache 2.0 (8/05), and **Qwen3.8-Flash-Next** (8/24 weights, 8/26 blog) previews the Qwen4 architecture: 125B main + 51B n-gram embeddings with only 6B active, served as Qwen3.8-Flash at $0.15/$0.47 per 1M. Qwen 3.7 Max remains the GA API flagship ($2.50/$7.50)
Kimi K3 (Moonshot)
Moonshot's 2.8T-parameter Kimi K3 (launched 2026-07-16/17) is the largest open-weight model ever released -- 1M context, multimodal, $3/$15 per 1M via API, ranked best-available on Arena.AI at launch. WEIGHTS SHIPPED ~2026-07-26/27 on Hugging Face (2.8T total / 104B activated, safetensors) under a custom Kimi K3 License, not the Modified MIT of the K2 line
Nemotron (Nvidia)
Nvidia's open-weights family -- hybrid Mamba-Transformer MoE architecture, optimized for efficient reasoning on Nvidia hardware. Nemotron 3 Ultra (550B total / 55B active) shipped 2026-06-04 as the family flagship, joining Super (120B/12B, March) and Nano
MiniMax M3
**MiniMax-H3's weights are public on Hugging Face (repo 2026-07-28, updated 2026-08-13) under a community licence whose open-weight grant is limited to the US, EU, UK and South Korea**, and **MiniMax Music 3 (2026-08-07)** ships open weights for five-minute full-song generation with a prominent-attribution commercial clause. MiniMax's coding/agent flagship -- M3 (June 1 2026): 1M-token context, MSA sparse attention (>15x decoding speedup at long context), SWE-Bench Pro 59.0%, Terminal-Bench 66.0%. OPEN WEIGHTS LIVE on HuggingFace since June 12 (~428B total / ~23B active, native multimodal, minimax-community license)
Falcon (TII)
UAE's Technology Innovation Institute open-weights family -- Falcon 3 optimized for efficient sub-10B deployment on consumer hardware
gpt-oss (OpenAI)
OpenAI's FIRST open-weight models -- gpt-oss-120b (single 80GB GPU, near parity with o4-mini on reasoning) and gpt-oss-20b (runs on 16GB edge devices). Apache 2.0. Launched 2025-08-05. gpt-oss-safeguard ships in 2026 as the safety-tuned variant
IBM Granite 4.0
IBM's enterprise-focused open-weight family -- Granite 4.0 hybrid Mamba-2 + transformer architecture (70-80% memory reduction vs pure transformer), 3B to 32B sizes, Apache 2.0. First open model family to secure ISO 42001 certification. Nano 350M runs on CPU with 8-16GB RAM. 3B Vision variant landed 2026-04-01
Arcee Trinity-Large-Thinking
Arcee AI's US-made open-weight frontier reasoning model -- launched 2026-04-01. 398B total params, ~13B active. Sparse MoE (256 experts, 4 active = 1.56% routing). Apache 2.0, trained from scratch. #2 on PinchBench trailing only Claude 3.5 Opus. ~96% cheaper than Opus-4.6 on agentic tasks
Olmo 3 (AI2)
Allen Institute for AI's fully-open frontier reasoning models -- Olmo 3 family (2025-11-20) includes 7B and 32B sizes, four variants (Base, Think, Instruct, RLZero). Apache 2.0 with fully open data + checkpoints + training logs. Olmo 3-Think 32B matches Qwen3-32B-Thinking at 6x fewer training tokens
AI21 Jamba2
AI21 Labs' hybrid SSM-Transformer (Mamba-style) open-weight family -- Jamba2 launched 2026-01-08. Two sizes: 3B dense (runs on phones / laptops) and Jamba2 Mini MoE (12B active / 52B total). Apache 2.0, 256K context, mid-trained on 500B tokens
StepFun Step 3.7 Flash
StepFun's (China) agent-focused open-weight family -- Step 3.7 Flash (May 28 2026): 198B sparse MoE vision-language model, ~11B active, 256K context, Apache 2.0, ~400 tok/s, SWE-Bench Pro 56.3. Supersedes Step 3.5 Flash (Feb 2026) as the flagship
Cohere Command A
Cohere's enterprise-multilingual flagship -- 111B params, 256K context, runs on 2x H100. 23 languages. CC-BY-NC 4.0 on weights (research / non-commercial), commercial requires Cohere enterprise contract. Follow-ups: Command A Reasoning + Command A Vision
LongCat-2.0 (Meituan)
Meituan's open-source 1.6T-parameter MoE (~48B active) with native 1M-token context, MIT license -- trained entirely on domestic Chinese AI ASICs and revealed as the stealth 'Owl Alpha' model that had been topping OpenRouter
Inkling (Thinking Machines Lab)
Mira Murati's $12B lab ships its first model (2026-07-15): a 975B/41B-active open-weights MoE that reasons natively over text, images, and audio with a 1M-token context -- positioned not as the strongest model, but as the best starting point for fine-tuning via Tinker
Bonsai 27B (PrismML)
The first 27B-class model that runs on a phone (2026-07-14) -- ternary and 1-bit quantizations of Qwen3.6 27B squeeze a multimodal, tool-calling, 262K-context model into 3.9-5.9GB under Apache 2.0