GLM 4.5 vs Mistral Medium 3.5
Mistral Medium 3.5 leads GLM 4.5 on the Artificial Analysis Intelligence Index, 14.2 to 12.8, and GLM 4.5 costs 3.4x less per output token ($2.20 vs $7.50 per million). Updated October 21, 2026; 47 benchmarks have a result for both.
GLM 4.5 or Mistral Medium 3.5: which to pick
Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; GLM 4.5 has none, so the two cannot be ranked on coding.
Pick GLM 4.5 for output tokens 3.4x cheaper ($2.20 vs $7.50 per million) and work measured by GPQA Diamond or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 47
- GLM 4.5 only
- 14
- Mistral Medium 3.5 only
- 34
- Like-for-like categories
- 8 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 Mistral Medium 3.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Mistral Medium 3.5
Mistral Medium 3.5 leads, 14.2 against 12.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Medium 3.5 has a AA Agentic Index result, 7.0.
Directional only
Reasoning
GPQA Diamond
GLM 4.5
GLM 4.5 leads, 72.2% against 34.9%.
Same board, both measured
Math
AIME
No call
Only GLM 4.5 has a AIME result, 86.7%.
Directional only
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 1142.
Same board, both measured
Long documents
Listed context window
Mistral Medium 3.5
Mistral Medium 3.5 takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5
GLM 4.5 $0.0017, Mistral Medium 3.5 $0.0053 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Mistral Medium 3.5 has the number, $6.02.
Directional only
At a glance
- Coding index
- GLM 4.5 not benchmarked, Mistral Medium 3.5 25.5 from 3 benchmarks.
- Widest category
- Agentic work, 51.2 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.5 43.0% against Mistral Medium 3.5 94.2%, 51.2 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 $2.20 against Mistral Medium 3.5 $7.50 per million output tokens, 3.4x apart.
- Context window
- GLM 4.5 takes 131K tokens, Mistral Medium 3.5 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 Medium 3.5 | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 4 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 6 | 1 | ||
| Web design | 7 | 0 | ||
| General | 9 | 9 | ||
| All shared boards | 26 | 21 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | GLM 4.5 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| SWE-bench Verified | 64.2% | Not measured | Only GLM 4.5 measured |
| LMArena WebDev | Not measured | 1264 | Only Mistral Medium 3.5 measured |
| AA Coding Index | Not measured | 46.9 | Only Mistral Medium 3.5 measured |
| LiveCodeBench (Vals AI) | 67.5% | Not measured | Only GLM 4.5 measured |
| SWE-bench Verified (Vals AI) | Not measured | 66.4% | Only Mistral Medium 3.5 measured |
| Vibe Code Bench (Vals AI) | Not measured | 2.9% | Only Mistral Medium 3.5 measured |
| Code migration (Vals AI) | Not measured | 5.1% | Only Mistral Medium 3.5 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 39.0% | Only Mistral Medium 3.5 measured |
| LiveCodeBench (Artificial Analysis run) | 73.8% | Not measured | Only GLM 4.5 measured |
| SciCode | Not measured | 40.2% | Only Mistral Medium 3.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | 33.3% | Mistral Medium 3.5 |
| WeirdML | 40.6% | Not measured | Only GLM 4.5 measured |
| ALE-Bench | 344.8 | 764.0 | Mistral Medium 3.5 |
| LMArena Text: coding | 1434 | 1460 | Mistral Medium 3.5 |
| LMArena Text: software and IT | 1440 | 1458 | Mistral Medium 3.5 |
| LMArena WebDev: React | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena WebDev: HTML | Not measured | 1308 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.8 | 14.2 | Mistral Medium 3.5 |
| Epoch Capabilities Index | Not measured | 141.4 | Only Mistral Medium 3.5 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 7.0 | Only Mistral Medium 3.5 measured |
| Finance Agent (Vals AI) | Not measured | 32.1% | Only Mistral Medium 3.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 4.5% | Only Mistral Medium 3.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 9.1% | Only Mistral Medium 3.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 11.2% | Only Mistral Medium 3.5 measured |
| τ²-Bench Telecom | 43.0% | 94.2% | Mistral Medium 3.5 |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index43
| Benchmark | GLM 4.5 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | 34.9% | GLM 4.5 |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 74.8% | GLM 4.5 |
| Humanity's Last Exam (Artificial Analysis run) | 13.0% | 13.8% | Mistral Medium 3.5 |
| LMArena Text: hard prompts | 1429 | 1436 | Mistral Medium 3.5 |
| LMArena Text: expert | 1433 | 1432 | GLM 4.5 |
| LMArena Text: hard prompts in English | 1429 | 1445 | Mistral Medium 3.5 |
| LMArena Vision: diagrams | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena Vision: homework | Not measured | 1230 | Only Mistral Medium 3.5 measured |
| Kagi LLM Benchmark | 57.9% | 41.4% | GLM 4.5 |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.5 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AIME | 86.7% | Not measured | Only GLM 4.5 measured |
| MATH 500 (Vals AI) | 94.0% | Not measured | Only GLM 4.5 measured |
| MGSM (Vals AI) | 90.8% | Not measured | Only GLM 4.5 measured |
| 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 |
| ProofBench | Not measured | 9.0% | Only Mistral Medium 3.5 measured |
| LMArena Text: math | 1427 | 1431 | Mistral Medium 3.5 |
| LMArena Text: mathematical work | 1430 | 1440 | Mistral Medium 3.5 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index61
| Benchmark | GLM 4.5 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| MMLU Pro | 81.2% | 75.3% | GLM 4.5 |
| LegalBench (Vals AI) | 75.6% | Not measured | Only GLM 4.5 measured |
| CaseLaw (Vals AI) | Not measured | 44.2% | Only Mistral Medium 3.5 measured |
| CorpFin (Vals AI) | 61.0% | 58.8% | GLM 4.5 |
| TaxEval (Vals AI) | 72.4% | 68.0% | GLM 4.5 |
| MortgageTax (Vals AI) | Not measured | 28.9% | Only Mistral Medium 3.5 measured |
| MedQA (Vals AI) | 90.0% | Not measured | Only GLM 4.5 measured |
| MedCode (Vals AI) | Not measured | 33.8% | Only Mistral Medium 3.5 measured |
| MMLU Pro (Artificial Analysis run) | 83.5% | Not measured | Only GLM 4.5 measured |
| LMArena Text: science | 1444 | 1428 | GLM 4.5 |
| LMArena Text: medicine and healthcare | 1455 | 1409 | GLM 4.5 |
| LMArena Text: legal and government | 1438 | 1437 | GLM 4.5 |
| LMArena Text: business and finance | 1420 | 1426 | Mistral Medium 3.5 |
| 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 Medium 3.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1170 | 1142 | GLM 4.5 |
| Design Arena: UI components | 1144 | 1079 | GLM 4.5 |
| Design Arena: game dev | 1145 | 1079 | GLM 4.5 |
| Design Arena: data viz | 1159 | 1120 | GLM 4.5 |
| Design Arena: 3D | 1170 | 1061 | GLM 4.5 |
| Design Arena: all code categories | 1165 | 1121 | GLM 4.5 |
| Design Arena: SVG | 1096 | 950 | GLM 4.5 |
| Design Arena: ASCII art | Not measured | 1063 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index99
| Benchmark | GLM 4.5 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 37.6% | Only Mistral Medium 3.5 measured |
| MedScribe (Vals AI) | Not measured | 67.7% | Only Mistral Medium 3.5 measured |
| Vals Multimodal Index | Not measured | 34.8% | Only Mistral Medium 3.5 measured |
| IFBench | 44.1% | 68.8% | Mistral Medium 3.5 |
| Long context reasoning (AA-LCR) | 52.7% | 69.3% | Mistral Medium 3.5 |
| DTBench | Not measured | 75.5% | Only Mistral Medium 3.5 measured |
| LMCA | Not measured | 26.1% | Only Mistral Medium 3.5 measured |
| Creative writing (Lech Mazur) | 73.4% | Not measured | Only GLM 4.5 measured |
| Surface Evolver Bench | Not measured | 26.9% | Only Mistral Medium 3.5 measured |
| LMArena Text | 1430 | 1421 | GLM 4.5 |
| LMArena Text: instruction following | 1404 | 1415 | Mistral Medium 3.5 |
| LMArena Text: creative writing | 1395 | 1374 | GLM 4.5 |
| LMArena Vision | Not measured | 1222 | Only Mistral Medium 3.5 measured |
| LMArena Text: English | 1437 | 1433 | GLM 4.5 |
| LMArena Text: non-English | 1417 | 1404 | GLM 4.5 |
| LMArena Text: Chinese | 1465 | 1444 | GLM 4.5 |
| LMArena Text: German | 1407 | 1453 | Mistral Medium 3.5 |
| LMArena Text: Japanese | 1415 | Not measured | Only GLM 4.5 measured |
| LMArena Text: Korean | 1380 | 1384 | Mistral Medium 3.5 |
| LMArena Text: French | 1418 | 1448 | Mistral Medium 3.5 |
| LMArena Text: Spanish | 1454 | 1411 | GLM 4.5 |
| LMArena Text: Russian | 1413 | 1395 | GLM 4.5 |
| LMArena Text: Polish | 1422 | 1430 | Mistral Medium 3.5 |
| LMArena Text: multi-turn | 1415 | 1423 | Mistral Medium 3.5 |
| LMArena Text: longer queries | 1412 | 1415 | Mistral Medium 3.5 |
| LMArena Text: writing and language | 1403 | 1384 | GLM 4.5 |
| LMArena Text: entertainment and media | 1404 | 1382 | GLM 4.5 |
| LMArena Vision: English | Not measured | 1240 | Only Mistral Medium 3.5 measured |
| LMArena Vision: Chinese | Not measured | 1244 | Only Mistral Medium 3.5 measured |
| LMArena Vision: reading text | Not measured | 1233 | Only Mistral Medium 3.5 measured |
| LMArena Vision: humor | Not measured | 1216 | Only Mistral Medium 3.5 measured |
| LMArena Vision: creative writing | Not measured | 1198 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 | Mistral Medium 3.5 | 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
GLM 4.5 costs less
Repository review
50K input + 3K output tokens
GLM 4.5 costs less
Agent loop
200K input + 10K output tokens
GLM 4.5 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 3.5 |
|---|---|---|
| Maker | Z.ai | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | July 25, 2025 | April 30, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.60 | $1.50 |
| Output $/Mtok | $2.20 | $7.50 |
| Coding index | Not benchmarked | 25.5 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $6.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 better than Mistral Medium 3.5 for coding?
Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; GLM 4.5 has none, so the two cannot be ranked on coding. Pick GLM 4.5 for output tokens 3.4x cheaper ($2.20 vs $7.50 per million) and work measured by GPQA Diamond or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, GLM 4.5 or Mistral Medium 3.5?
GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Cost per solved task is known for Mistral Medium 3.5 only.
Which has the bigger context window, GLM 4.5 or Mistral Medium 3.5?
Mistral Medium 3.5, with 262K tokens against 131K.
How many benchmarks is the GLM 4.5 vs Mistral Medium 3.5 comparison based on?
47 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 3.5's on 3. Where only one model has a result, the page says so instead of naming a winner.