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Mistral Large 4 vs GLM-5.3
GLM-5.3 scores higher than Mistral Large 4 on all 4 public leaderboards that rate both. On the ArtificialWatch Index, which takes the median of each model's percentile across the boards that score it, Mistral Large 4 is at 20.9 and GLM-5.3 at 46.7 (out of 100). On each maker's API, Mistral Large 4 costs $0.70 / $2.10 per 1M tokens (input / output) and GLM-5.3 $1.40 / $4.40.
Scores as published by each board; table composed November 13, 2026.
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Leaderboard by leaderboard
| Board | Mistral Large 4 | GLM-5.3 | Higher |
|---|---|---|---|
| LMArena Elo (community votes) | 1,564.5 | 1,622.5 | GLM-5.3 |
| Vals AI Vals Index accuracy % · weighted finance + coding tasks | 56.5 | 59.8 | GLM-5.3 |
| Artificial Analysis intelligence index · measured runs only | 26.1 | 31.7 | GLM-5.3 |
| Epoch AI Epoch Capabilities Index (ECI) · many benchmarks on one scale | 154.6 | 157.1 | GLM-5.3 |
Raw scores are each board's own scale, so compare within a row, not across rows. Full table: the bench index.
For coding
On the ArtificialWatch coding index, Mistral Large 4 is at 14 (#46 of 48) and GLM-5.3 at 50.4 (#20). Mistral Large 4 scores higher on 1 of the 2 coding boards that rate both and GLM-5.3 on 1.
| Coding board | Mistral Large 4 | GLM-5.3 |
|---|---|---|
| LMArena · Coding | 1,457.5 | 1,498.5 |
| Vals Vibe Code Bench | 81.6 | 78.1 |
Mistral Large 4 at a glance
- Developer
- Mistral
- Status
- Live since October 6, 2026
- API price
- $0.70 input / $2.10 output per 1M tokens · Mistral, mistral-large-4
- Context window
- 1.05M tokens
Live — Oct 6, public preview on Mistral's API and OpenRouter · 1T total / 49B active · open weights since Oct 29 on Hugging Face (Mistral Ecosystem Open-Weight License, a modified MIT; running it as a paid API service needs a separate license), an improved checkpoint after three more weeks of RL by Mistral's account · $0.70 / $2.10 per 1M from Oct 29 ($0.07 cached, Mistral's docs) · Artificial Analysis Intelligence Index v5.0: 26 (read Oct 29) · Epoch Capabilities Index (read Oct 15): 146.9, 71st of 277, level with OpenAI's o3 from April 2025 (146.9) and behind the open-weight Kimi K3 (157.4), GLM-5.3 (155.9) and DeepSeek V4 Pro (155.3).
Mistral Large 4's weights are out, and Mistral says they beat its own preview
2026-10-29 · Mistral published the weights of Mistral Large 4 on Hugging Face on October 29, two days inside the end-of-October date it gave when the model went to public preview on October 6. The repository, mistralai/Mistral-Large-4-1T-A52B, holds the full checkpoint in 54 shards and is not gated.
Mistral Large 4 is out in public preview: 1T parameters, $0.68 in and $2.09 out for now, open weights by the end of October
2026-10-06 · Mistral launched Mistral Large 4 today as a public preview on its own API. Per Mistral's docs it is a natively multimodal mixture-of-experts model with about 1.05 trillion total parameters and 49 billion active, plus a 1.6B vision encoder. Mistral says it will release the open weights by the end of this month.
GLM-5.3 at a glance
- Developer
- Zhipu AI
- Status
- Live since August 14, 2026
- API price
- $1.40 input / $4.40 output per 1M tokens · Z.AI, glm-5.3
- Context window
- 1M tokens
Live - same 743B base as 5.2, every gain from post-training · tops CyberGym · open weights on Hugging Face (zai-org/GLM-5.3, 141 safetensors files, public when we read it Sep 25; repo created Aug 25) · Anthropic research (Sep 29): builds end-to-end exploits near Claude Mythos Preview's rate (ExploitBench 50 of 410 vs 56 of 410), and its safeguards fell to simple bypasses 64-100% of the time · Epoch AI report (Oct 29): built one-click file-reading exploits for all six Firefox releases Sep 17-Oct 26 from published-but-unshipped fixes, about $1,000 each, with no refusals at any provider.
Epoch AI: open-weight GLM 5.3 built working Firefox exploits from security fixes that had not shipped yet, for about $1,000 each
2026-10-30 · An open-weight model can now turn browser security fixes that are public but not yet shipped into working exploits on its own, according to a report Epoch AI published October 29.
Anthropic: open-weights GLM-5.3 nearly matches Claude Mythos Preview at building exploits, and its safeguards are easy to strip
2026-09-29 · Anthropic published research on September 29 testing Z.ai's GLM-5.3, an open-weights model anyone can download, on offensive cyber tasks. Its finding: GLM-5.3 can build working end-to-end exploits at close to the rate of Claude Mythos Preview, the model Anthropic released only to vetted defenders.
Mistral Large 4 vs GLM-5.3: quick answers
Which is better, Mistral Large 4 or GLM-5.3?
It depends on what you measure. GLM-5.3 scores higher than Mistral Large 4 on all 4 public leaderboards that rate both. GLM-5.3's widest lead is on Artificial Analysis. On the ArtificialWatch Index, which takes the median of each model's percentile across the boards that score it, Mistral Large 4 is at 20.9 and GLM-5.3 at 46.7 (out of 100).
Which is newer, Mistral Large 4 or GLM-5.3?
Mistral Large 4. It went live on October 6, 2026, 53 days after GLM-5.3 (August 14, 2026).
Which is cheaper, Mistral Large 4 or GLM-5.3?
Mistral Large 4 is cheaper on both input and output. On each vendor's own API, Mistral Large 4 is $0.70 input / $2.10 output per million tokens and GLM-5.3 is $1.40 input / $4.40 output per million tokens. Prices as published in the models.dev catalog, as of 2026-11-12.
Which has the bigger context window, Mistral Large 4 or GLM-5.3?
Mistral Large 4, at 1.05M tokens against GLM-5.3's 1M, as each vendor lists it.
How do Mistral Large 4 and GLM-5.3 compare for coding?
On the ArtificialWatch coding index, Mistral Large 4 is at 14 (#46 of 48) and GLM-5.3 at 50.4 (#20). Mistral Large 4 scores higher on 1 of the 2 coding boards that rate both and GLM-5.3 on 1.
Who makes Mistral Large 4 and GLM-5.3?
Mistral Large 4 is from Mistral; GLM-5.3 is from Zhipu AI.
Where do these numbers come from?
Each score is the leaderboard's own published figure, read by ArtificialWatch (table composed November 13, 2026). Boards measure different things, so raw scores are only comparable within a row.
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