GLM 4.5 vs Mistral Large 3 2512
Updated October 21, 2026. 18 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
GLM 4.5 has the edge where what matters is LegalBench (Vals AI) or CorpFin (Vals AI), where it leads.
- Shared results
- 18
- GLM 4.5 only
- 42
- Mistral Large 3 2512 only
- 10
- Like-for-like categories
- 6 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.5
GLM 4.5 leads, 12.8 against 9.3.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Large 3 2512 has a AA Agentic Index result, 1.0.
Directional only
Reasoning
GPQA Diamond
GLM 4.5
GLM 4.5 leads, 72.2% against 68.4%.
Same board, both measured
Math
AIME
GLM 4.5
GLM 4.5 leads, 86.7% against 42.9%.
Same board, both measured
Knowledge
MMLU Pro
GLM 4.5
GLM 4.5 leads, 81.2% against 79.8%.
Same board, both measured
Web design
Design Arena: websites
GLM 4.5
GLM 4.5 leads, 1170 against 1162.
Same board, both measured
Long documents
Listed context window
Mistral Large 3 2512
Mistral Large 3 2512 takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large 3 2512
GLM 4.5 $0.0017, Mistral Large 3 2512 $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Mistral Large 3 2512 has the number, $0.77.
Directional only
At a glance
- Coding index
- GLM 4.5 not benchmarked, Mistral Large 3 2512 not benchmarked.
- Widest category
- Math, 24.6 points apart on average over 2 shared benchmarks.
- Widest single gap
- AIME: GLM 4.5 86.7% against Mistral Large 3 2512 42.9%, 43.8 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 $2.20 against Mistral Large 3 2512 $1.50 per million output tokens, 1.5x apart.
- Context window
- GLM 4.5 takes 131K 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 4.5 | Bars | Mistral Large 3 2512 | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 2 | 3 | ||
| Web design | 7 | 0 | ||
| All shared boards | 15 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GLM 4.5 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| SWE-bench Verified | 64.2% | Not measured | Only GLM 4.5 measured |
| AA Coding Index | Not measured | 20.1 | Only Mistral Large 3 2512 measured |
| LiveCodeBench (Vals AI) | 67.5% | 55.3% | GLM 4.5 |
| SWE-bench Verified (Vals AI) | Not measured | 41.4% | Only Mistral Large 3 2512 measured |
| LiveCodeBench (Artificial Analysis run) | 73.8% | Not measured | Only GLM 4.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | Not measured | Only GLM 4.5 measured |
| WeirdML | 40.6% | Not measured | Only GLM 4.5 measured |
| ALE-Bench | 344.8 | 264.7 | GLM 4.5 |
| LMArena Text: coding | 1434 | Not measured | Only GLM 4.5 measured |
| LMArena Text: software and IT | 1440 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.5 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.8 | 9.3 | GLM 4.5 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 4.5 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.0 | Only Mistral Large 3 2512 measured |
| τ²-Bench Telecom | 43.0% | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.5 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | 68.4% | GLM 4.5 |
| MMMU Pro (Vals AI) | Not measured | 66.2% | Only Mistral Large 3 2512 measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | Not measured | Only GLM 4.5 measured |
| Humanity's Last Exam (Artificial Analysis run) | 13.0% | Not measured | Only GLM 4.5 measured |
| LMArena Text: hard prompts | 1429 | Not measured | Only GLM 4.5 measured |
| LMArena Text: expert | 1433 | Not measured | Only GLM 4.5 measured |
| LMArena Text: hard prompts in English | 1429 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.5 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AIME | 86.7% | 42.9% | GLM 4.5 |
| MATH 500 (Vals AI) | 94.0% | Not measured | Only GLM 4.5 measured |
| MGSM (Vals AI) | 90.8% | 85.4% | GLM 4.5 |
| AA Math Index | 73.7 | Not measured | Only GLM 4.5 measured |
| AIME 2025 (Artificial Analysis run) | 73.7% | Not measured | Only GLM 4.5 measured |
| LMArena Text: math | 1427 | Not measured | Only GLM 4.5 measured |
| LMArena Text: mathematical work | 1430 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | GLM 4.5 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| MMLU Pro | 81.2% | 79.8% | GLM 4.5 |
| LegalBench (Vals AI) | 75.6% | 79.1% | Mistral Large 3 2512 |
| CaseLaw (Vals AI) | Not measured | 61.4% | Only Mistral Large 3 2512 measured |
| CorpFin (Vals AI) | 61.0% | 61.0% | Mistral Large 3 2512 |
| 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) | 90.0% | 82.2% | GLM 4.5 |
| MMLU Pro (Artificial Analysis run) | 83.5% | Not measured | Only GLM 4.5 measured |
| LMArena Text: science | 1444 | Not measured | Only GLM 4.5 measured |
| LMArena Text: medicine and healthcare | 1455 | Not measured | Only GLM 4.5 measured |
| LMArena Text: legal and government | 1438 | Not measured | Only GLM 4.5 measured |
| LMArena Text: business and finance | 1420 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 4.5 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| Design Arena: websites | 1170 | 1162 | GLM 4.5 |
| Design Arena: UI components | 1144 | 1102 | GLM 4.5 |
| Design Arena: game dev | 1145 | 1080 | GLM 4.5 |
| Design Arena: data viz | 1159 | 1136 | GLM 4.5 |
| Design Arena: 3D | 1170 | 1099 | GLM 4.5 |
| Design Arena: all code categories | 1165 | 1142 | GLM 4.5 |
| Design Arena: SVG | 1096 | 993 | GLM 4.5 |
| Design Arena: ASCII art | Not measured | 1069 | Only Mistral Large 3 2512 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index23 boards, none shared
| Benchmark | GLM 4.5 | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 24.6% | Only Mistral Large 3 2512 measured |
| IFBench | 44.1% | Not measured | Only GLM 4.5 measured |
| Long context reasoning (AA-LCR) | 52.7% | Not measured | Only GLM 4.5 measured |
| DTBench | Not measured | 65.1% | Only Mistral Large 3 2512 measured |
| LMCA | Not measured | 16.7% | Only Mistral Large 3 2512 measured |
| Creative writing (Lech Mazur) | 73.4% | Not measured | Only GLM 4.5 measured |
| LMArena Text | 1430 | Not measured | Only GLM 4.5 measured |
| LMArena Text: instruction following | 1404 | Not measured | Only GLM 4.5 measured |
| LMArena Text: creative writing | 1395 | Not measured | Only GLM 4.5 measured |
| LMArena Text: English | 1437 | Not measured | Only GLM 4.5 measured |
| LMArena Text: non-English | 1417 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Chinese | 1465 | Not measured | Only GLM 4.5 measured |
| LMArena Text: German | 1407 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Japanese | 1415 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Korean | 1380 | Not measured | Only GLM 4.5 measured |
| LMArena Text: French | 1418 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Spanish | 1454 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Russian | 1413 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Polish | 1422 | Not measured | Only GLM 4.5 measured |
| LMArena Text: multi-turn | 1415 | Not measured | Only GLM 4.5 measured |
| LMArena Text: longer queries | 1412 | Not measured | Only GLM 4.5 measured |
| LMArena Text: writing and language | 1403 | Not measured | Only GLM 4.5 measured |
| LMArena Text: entertainment and media | 1404 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 | 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 4.5 | Mistral Large 3 2512 |
|---|---|---|
| Maker | Z.ai | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | July 25, 2025 | December 1, 2025 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.60 | $0.50 |
| Output $/Mtok | $2.20 | $1.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | $0.77 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 better than Mistral Large 3 2512 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.5 has the edge where what matters is LegalBench (Vals AI) or CorpFin (Vals AI), where it leads.
Which is cheaper, GLM 4.5 or Mistral Large 3 2512?
GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Mistral Large 3 2512 costs $0.50 and $1.50. Cost per solved task is known for Mistral Large 3 2512 only.
Which has the bigger context window, GLM 4.5 or Mistral Large 3 2512?
Mistral Large 3 2512, with 262K tokens against 131K.
How many benchmarks is the GLM 4.5 vs Mistral Large 3 2512 comparison based on?
18 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 0 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.