GLM 5.3 vs Mistral Large 3 2512
GLM 5.3 leads Mistral Large 3 2512 on the Artificial Analysis Intelligence Index, 44.8 to 9.3, and Mistral Large 3 2512 costs 2.9x less per output token ($1.50 vs $4.40 per million). Updated October 21, 2026; 20 benchmarks have a result for both.
GLM 5.3 or Mistral Large 3 2512: which to pick
Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; Mistral Large 3 2512 has none, so the two cannot be ranked on coding.
Pick Mistral Large 3 2512 for output tokens 2.9x cheaper ($1.50 vs $4.40 per million) and work measured by TaxEval (Vals AI), where it leads.
- Shared results
- 20
- GLM 5.3 only
- 80
- Mistral Large 3 2512 only
- 8
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GLM 5.3 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 9.3.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3
GLM 5.3 leads, 53.1 against 1.0.
Same board, both measured
Reasoning
GPQA Diamond
GLM 5.3
GLM 5.3 leads, 88.1% against 68.4%.
Same board, both measured
Math
AIME
No call
Only Mistral Large 3 2512 has a AIME result, 42.9%.
Directional only
Knowledge
MMLU Pro
GLM 5.3
GLM 5.3 leads, 86.8% against 79.8%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1162.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
Directional only
Long documents
Listed context window
GLM 5.3
GLM 5.3 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large 3 2512
GLM 5.3 $0.0036, Mistral Large 3 2512 $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
GLM 5.3 $0.36, Mistral Large 3 2512 $0.77.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 70.6 from 6 benchmarks, Mistral Large 3 2512 not benchmarked.
- Widest category
- Agentic work, 52.1 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Coding Index: GLM 5.3 74.8 against Mistral Large 3 2512 20.1, 54.7 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $4.40 against Mistral Large 3 2512 $1.50 per million output tokens, 2.9x apart.
- Context window
- GLM 5.3 takes 1.05M tokens, Mistral Large 3 2512 262K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GLM 5.3 | Bars | Mistral Large 3 2512 | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| Knowledge | 2 | 1 | ||
| Web design | 8 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 19 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GLM 5.3 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| LiveBench coding | 82.8% | Not measured | Only GLM 5.3 measured |
| LMArena WebDev | 1621 | Not measured | Only GLM 5.3 measured |
| AA Coding Index | 74.8 | 20.1 | GLM 5.3 |
| LiveCodeBench (Vals AI) | 80.5% | 55.3% | GLM 5.3 |
| SWE-bench Verified (Vals AI) | 95.4% | 41.4% | GLM 5.3 |
| Terminal-Bench 4 (Vals AI) | 38.9% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench (Vals AI) | 78.1% | Not measured | Only GLM 5.3 measured |
| Code migration (Vals AI) | 44.2% | Not measured | Only GLM 5.3 measured |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GLM 5.3 measured |
| IOI (Vals AI) | 68.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 2.1 (Vals AI) | 71.5% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GLM 5.3 measured |
| SkillsBench (Vals AI) | 47.5% | Not measured | Only GLM 5.3 measured |
| CyberBench (Vals AI) | 72.1% | Not measured | Only GLM 5.3 measured |
| SciCode | 59.0% | Not measured | Only GLM 5.3 measured |
| FrontierCode | 40.1% | Not measured | Only GLM 5.3 measured |
| FrontierSWE | 30.2% | Not measured | Only GLM 5.3 measured |
| WeirdML | 75.4% | Not measured | Only GLM 5.3 measured |
| ALE-Bench | 1317.4 | 264.7 | GLM 5.3 |
| LMArena Text: coding | 1497 | Not measured | Only GLM 5.3 measured |
| LMArena Text: software and IT | 1493 | Not measured | Only GLM 5.3 measured |
| LMArena WebDev: React | 1641 | Not measured | Only GLM 5.3 measured |
| LMArena WebDev: HTML | 1595 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.3 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 9.3 | GLM 5.3 |
| Epoch Capabilities Index | 156.0 | Not measured | Only GLM 5.3 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.3 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | 1.0 | GLM 5.3 |
| Design Arena: agents building mobile apps | 1194 | Not measured | Only GLM 5.3 measured |
| Design Arena: HTML slides | 1189 | Not measured | Only GLM 5.3 measured |
| Design Arena: PowerPoint slides | 1254 | Not measured | Only GLM 5.3 measured |
| LiveBench research | 73.2% | Not measured | Only GLM 5.3 measured |
| LiveBench browser | 0.0% | Not measured | Only GLM 5.3 measured |
| LiveBench long agentic | 35.9% | Not measured | Only GLM 5.3 measured |
| SREBench (Vals AI) | 9.2% | Not measured | Only GLM 5.3 measured |
| Finance Agent (Vals AI) | 55.8% | Not measured | Only GLM 5.3 measured |
| Tax Agent Bench (Vals AI) | 39.7% | Not measured | Only GLM 5.3 measured |
| Legal Research Bench (Vals AI) | 49.0% | Not measured | Only GLM 5.3 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | Not measured | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | 56.3% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only GLM 5.3 measured |
| APEX-Agents | 56.6% | Not measured | Only GLM 5.3 measured |
| Vending-Bench 2 | $8,164 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.3 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | 68.4% | GLM 5.3 |
| LiveBench reasoning | 85.8% | Not measured | Only GLM 5.3 measured |
| MysteryMechanism (Vals AI) | 23.0% | Not measured | Only GLM 5.3 measured |
| MMMU Pro (Vals AI) | Not measured | 66.2% | Only Mistral Large 3 2512 measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | Not measured | Only GLM 5.3 measured |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | Not measured | Only GLM 5.3 measured |
| GPQA Diamond (Epoch AI run) | 90.9% | Not measured | Only GLM 5.3 measured |
| Chess puzzles | 37.0% | Not measured | Only GLM 5.3 measured |
| Mystery game puzzles | 33.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: hard prompts | 1490 | Not measured | Only GLM 5.3 measured |
| LMArena Text: expert | 1514 | Not measured | Only GLM 5.3 measured |
| LMArena Text: hard prompts in English | 1496 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 5.3 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AIME | Not measured | 42.9% | Only Mistral Large 3 2512 measured |
| MGSM (Vals AI) | Not measured | 85.4% | Only Mistral Large 3 2512 measured |
| OTIS Mock AIME | 91.1% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tiers 1 to 3 | 68.8% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.3 measured |
| ProofBench | 49.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: math | 1495 | Not measured | Only GLM 5.3 measured |
| LMArena Text: mathematical work | 1497 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GLM 5.3 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | 79.8% | GLM 5.3 |
| LegalBench (Vals AI) | 84.8% | 79.1% | GLM 5.3 |
| CaseLaw (Vals AI) | Not measured | 61.4% | Only Mistral Large 3 2512 measured |
| CorpFin (Vals AI) | Not measured | 61.0% | Only Mistral Large 3 2512 measured |
| TaxEval (Vals AI) | 72.4% | 73.1% | Mistral Large 3 2512 |
| MortgageTax (Vals AI) | Not measured | 52.1% | Only Mistral Large 3 2512 measured |
| MedQA (Vals AI) | Not measured | 82.2% | Only Mistral Large 3 2512 measured |
| MedCode (Vals AI) | 42.9% | Not measured | Only GLM 5.3 measured |
| SimpleQA Verified | 41.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: science | 1498 | Not measured | Only GLM 5.3 measured |
| LMArena Text: medicine and healthcare | 1481 | Not measured | Only GLM 5.3 measured |
| LMArena Text: legal and government | 1473 | Not measured | Only GLM 5.3 measured |
| LMArena Text: business and finance | 1459 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 5.3 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | 1162 | GLM 5.3 |
| Design Arena: UI components | 1314 | 1102 | GLM 5.3 |
| Design Arena: game dev | 1311 | 1080 | GLM 5.3 |
| Design Arena: data viz | 1259 | 1136 | GLM 5.3 |
| Design Arena: 3D | 1332 | 1100 | GLM 5.3 |
| Design Arena: all code categories | 1304 | 1142 | GLM 5.3 |
| Design Arena: SVG | 1293 | 993 | GLM 5.3 |
| Design Arena: ASCII art | 1213 | 1069 | GLM 5.3 |
| LiveBench design | 77.3% | Not measured | Only GLM 5.3 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| Vals Index | 59.7% | Not measured | Only GLM 5.3 measured |
| LiveBench data analysis | 70.2% | Not measured | Only GLM 5.3 measured |
| Public Benefits Bench (Vals AI) | 68.5% | Not measured | Only GLM 5.3 measured |
| SAGE (Vals AI) | Not measured | 24.6% | Only Mistral Large 3 2512 measured |
| MedScribe (Vals AI) | 88.8% | Not measured | Only GLM 5.3 measured |
| Long context reasoning (AA-LCR) | 79.7% | Not measured | Only GLM 5.3 measured |
| DTBench | 87.7% | 65.1% | GLM 5.3 |
| LMCA | 55.5% | 16.7% | GLM 5.3 |
| LMArena Text | 1474 | Not measured | Only GLM 5.3 measured |
| LMArena Text: instruction following | 1479 | Not measured | Only GLM 5.3 measured |
| LMArena Text: creative writing | 1458 | Not measured | Only GLM 5.3 measured |
| LMArena Text: English | 1482 | Not measured | Only GLM 5.3 measured |
| LMArena Text: non-English | 1459 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Chinese | 1532 | Not measured | Only GLM 5.3 measured |
| LMArena Text: German | 1488 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Japanese | 1460 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Korean | 1460 | Not measured | Only GLM 5.3 measured |
| LMArena Text: French | 1503 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Spanish | 1462 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Russian | 1469 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Polish | 1466 | Not measured | Only GLM 5.3 measured |
| LMArena Text: multi-turn | 1472 | Not measured | Only GLM 5.3 measured |
| LMArena Text: longer queries | 1483 | Not measured | Only GLM 5.3 measured |
| LMArena Text: writing and language | 1469 | Not measured | Only GLM 5.3 measured |
| LMArena Text: entertainment and media | 1447 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Mistral Large 3 2512 costs less
Repository review
50K input + 3K output tokens
Mistral Large 3 2512 costs less
Agent loop
200K input + 10K output tokens
Mistral Large 3 2512 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Z.ai GLM 5.3 | Mistral Large 3 2512 |
|---|---|---|
| Maker | Z.ai | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | August 18, 2026 | December 1, 2025 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $1.40 | $0.50 |
| Output $/Mtok | $4.40 | $1.50 |
| Coding index | 70.6 | Not benchmarked |
| Independent measurements behind it | 6 | 1 |
| Cost per solved task | $0.36 | $0.77 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than Mistral Large 3 2512 for coding?
Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; Mistral Large 3 2512 has none, so the two cannot be ranked on coding. Pick Mistral Large 3 2512 for output tokens 2.9x cheaper ($1.50 vs $4.40 per million) and work measured by TaxEval (Vals AI), where it leads.
Which is cheaper, GLM 5.3 or Mistral Large 3 2512?
GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; Mistral Large 3 2512 costs $0.50 and $1.50. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and Mistral Large 3 2512 $0.77.
Which has the bigger context window, GLM 5.3 or Mistral Large 3 2512?
GLM 5.3, with 1.05M tokens against 262K.
How many benchmarks is the GLM 5.3 vs Mistral Large 3 2512 comparison based on?
20 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and Mistral Large 3 2512's on 1. Where only one model has a result, the page says so instead of naming a winner.