GLM 4.5 vs Mistral Medium 3.1
Updated October 21, 2026. 56 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 LMArena Text: hard prompts in English or MGSM (Vals AI), where it leads.
- Shared results
- 56
- GLM 4.5 only
- 4
- Mistral Medium 3.1 only
- 18
- Like-for-like categories
- 8 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 9.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Medium 3.1 has a AA Agentic Index result, 1.9.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 4.5 has a GPQA Diamond result, 72.2%.
Directional only
Math
AIME
GLM 4.5
GLM 4.5 leads, 86.7% against 42.3%.
Same board, both measured
Knowledge
MMLU Pro
GLM 4.5
GLM 4.5 leads, 81.2% against 75.3%.
Same board, both measured
Web design
Design Arena: websites
GLM 4.5
GLM 4.5 leads, 1170 against 1133.
Same board, both measured
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Medium 3.1
GLM 4.5 $0.0017, Mistral Medium 3.1 $0.0014 per turn.
List prices
At a glance
- Coding index
- GLM 4.5 not benchmarked, Mistral Medium 3.1 not benchmarked.
- Widest category
- Math, 18.3 points apart on average over 7 shared benchmarks.
- Widest single gap
- AIME: GLM 4.5 86.7% against Mistral Medium 3.1 42.3%, 44.4 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 $2.20 against Mistral Medium 3.1 $2.00 per million output tokens, 1.1x apart.
- Context window
- Both take 131K tokens.
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 Medium 3.1 | Ties |
|---|---|---|---|---|
| Coding | 6 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 4 | 1 | ||
| Math | 6 | 1 | ||
| Knowledge | 8 | 2 | ||
| Web design | 7 | 0 | ||
| General | 13 | 6 | ||
| All shared boards | 46 | 10 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index60
| Benchmark | GLM 4.5 | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| SWE-bench Verified | 64.2% | Not measured | Only GLM 4.5 measured |
| AA Coding Index | Not measured | 20.5 | Only Mistral Medium 3.1 measured |
| LiveCodeBench (Vals AI) | 67.5% | 44.8% | GLM 4.5 |
| LiveCodeBench (Artificial Analysis run) | 73.8% | 40.6% | GLM 4.5 |
| SciCode | Not measured | 32.4% | Only Mistral Medium 3.1 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | 10.6% | GLM 4.5 |
| WeirdML | 40.6% | Not measured | Only GLM 4.5 measured |
| ALE-Bench | 344.8 | 210.2 | GLM 4.5 |
| LMArena Text: coding | 1434 | 1433 | GLM 4.5 |
| LMArena Text: software and IT | 1440 | 1436 | GLM 4.5 |
| 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 Medium 3.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.8 | 9.2 | GLM 4.5 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.5 | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.9 | Only Mistral Medium 3.1 measured |
| τ²-Bench Telecom | 43.0% | 40.6% | GLM 4.5 |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GLM 4.5 | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | Not measured | Only GLM 4.5 measured |
| MMMU Pro (Vals AI) | Not measured | 63.0% | Only Mistral Medium 3.1 measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 58.8% | GLM 4.5 |
| Humanity's Last Exam (Artificial Analysis run) | 13.0% | 4.7% | GLM 4.5 |
| LMArena Text: hard prompts | 1429 | 1426 | GLM 4.5 |
| LMArena Text: expert | 1433 | 1408 | GLM 4.5 |
| LMArena Text: hard prompts in English | 1429 | 1435 | Mistral Medium 3.1 |
| LMArena Vision: diagrams | Not measured | 1175 | Only Mistral Medium 3.1 measured |
| LMArena Vision: homework | Not measured | 1165 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index61
| Benchmark | GLM 4.5 | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AIME | 86.7% | 42.3% | GLM 4.5 |
| MATH 500 (Vals AI) | 94.0% | 87.0% | GLM 4.5 |
| MGSM (Vals AI) | 90.8% | 90.9% | Mistral Medium 3.1 |
| AA Math Index | 73.7 | 38.3 | GLM 4.5 |
| AIME 2025 (Artificial Analysis run) | 73.7% | 38.3% | GLM 4.5 |
| LMArena Text: math | 1427 | 1408 | GLM 4.5 |
| LMArena Text: mathematical work | 1430 | 1412 | GLM 4.5 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index82
| Benchmark | GLM 4.5 | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| MMLU Pro | 81.2% | 75.3% | GLM 4.5 |
| LegalBench (Vals AI) | 75.6% | 62.0% | GLM 4.5 |
| CorpFin (Vals AI) | 61.0% | 50.7% | GLM 4.5 |
| TaxEval (Vals AI) | 72.4% | 70.3% | GLM 4.5 |
| MortgageTax (Vals AI) | Not measured | 36.5% | Only Mistral Medium 3.1 measured |
| MedQA (Vals AI) | 90.0% | 78.2% | GLM 4.5 |
| MMLU Pro (Artificial Analysis run) | 83.5% | 68.3% | GLM 4.5 |
| LMArena Text: science | 1444 | 1444 | Mistral Medium 3.1 |
| LMArena Text: medicine and healthcare | 1455 | 1449 | GLM 4.5 |
| LMArena Text: legal and government | 1438 | 1439 | Mistral Medium 3.1 |
| LMArena Text: business and finance | 1420 | 1418 | GLM 4.5 |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 4.5 | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1170 | 1133 | GLM 4.5 |
| Design Arena: UI components | 1144 | 1099 | GLM 4.5 |
| Design Arena: game dev | 1145 | 1074 | GLM 4.5 |
| Design Arena: data viz | 1159 | 1144 | GLM 4.5 |
| Design Arena: 3D | 1170 | 1081 | GLM 4.5 |
| Design Arena: all code categories | 1165 | 1120 | GLM 4.5 |
| Design Arena: SVG | 1096 | 993 | GLM 4.5 |
| Design Arena: ASCII art | Not measured | 997 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index136
| Benchmark | GLM 4.5 | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| IFBench | 44.1% | 39.8% | GLM 4.5 |
| Long context reasoning (AA-LCR) | 52.7% | 42.7% | GLM 4.5 |
| DTBench | Not measured | 67.5% | Only Mistral Medium 3.1 measured |
| LMCA | Not measured | 19.1% | Only Mistral Medium 3.1 measured |
| Creative writing (Lech Mazur) | 73.4% | Not measured | Only GLM 4.5 measured |
| LMArena Text | 1430 | 1424 | GLM 4.5 |
| LMArena Text: instruction following | 1404 | 1398 | GLM 4.5 |
| LMArena Text: creative writing | 1395 | 1391 | GLM 4.5 |
| LMArena Vision | Not measured | 1172 | Only Mistral Medium 3.1 measured |
| LMArena Text: English | 1437 | 1438 | Mistral Medium 3.1 |
| LMArena Text: non-English | 1417 | 1408 | GLM 4.5 |
| LMArena Text: Chinese | 1465 | 1447 | GLM 4.5 |
| LMArena Text: German | 1407 | 1432 | Mistral Medium 3.1 |
| LMArena Text: Japanese | 1415 | 1379 | GLM 4.5 |
| LMArena Text: Korean | 1380 | 1380 | Mistral Medium 3.1 |
| LMArena Text: French | 1418 | 1460 | Mistral Medium 3.1 |
| LMArena Text: Spanish | 1454 | 1433 | GLM 4.5 |
| LMArena Text: Russian | 1413 | 1411 | GLM 4.5 |
| LMArena Text: Polish | 1422 | 1430 | Mistral Medium 3.1 |
| LMArena Text: multi-turn | 1415 | 1418 | Mistral Medium 3.1 |
| LMArena Text: longer queries | 1412 | 1406 | GLM 4.5 |
| LMArena Text: writing and language | 1403 | 1395 | GLM 4.5 |
| LMArena Text: entertainment and media | 1404 | 1390 | GLM 4.5 |
| LMArena Vision: English | Not measured | 1179 | Only Mistral Medium 3.1 measured |
| LMArena Vision: Chinese | Not measured | 1190 | Only Mistral Medium 3.1 measured |
| LMArena Vision: reading text | Not measured | 1175 | Only Mistral Medium 3.1 measured |
| LMArena Vision: humor | Not measured | 1168 | Only Mistral Medium 3.1 measured |
| LMArena Vision: captioning | Not measured | 1145 | Only Mistral Medium 3.1 measured |
| LMArena Vision: recognizing things | Not measured | 1147 | Only Mistral Medium 3.1 measured |
| LMArena Vision: creative writing | Not measured | 1200 | Only Mistral Medium 3.1 measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 | Mistral Medium 3.1 | 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 Medium 3.1 costs less
Repository review
50K input + 3K output tokens
Mistral Medium 3.1 costs less
Agent loop
200K input + 10K output tokens
Mistral Medium 3.1 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 Medium 3.1 |
|---|---|---|
| Maker | Z.ai | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | July 25, 2025 | August 13, 2025 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.60 | $0.40 |
| Output $/Mtok | $2.20 | $2.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 better than Mistral Medium 3.1 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 LMArena Text: hard prompts in English or MGSM (Vals AI), where it leads.
Which is cheaper, GLM 4.5 or Mistral Medium 3.1?
GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Mistral Medium 3.1 costs $0.40 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5 or Mistral Medium 3.1?
Neither: both take 131K tokens.
How many benchmarks is the GLM 4.5 vs Mistral Medium 3.1 comparison based on?
56 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 Medium 3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.