GLM 5.3 vs Mistral Medium 3.5
GLM 5.3 leads Mistral Medium 3.5 on coding, 70.6 to 25.5 on the Vibe Coding Index, and GLM 5.3 costs 1.7x less per output token ($4.40 vs $7.50 per million). Updated October 21, 2026; 63 benchmarks have a result for both.
GLM 5.3 or Mistral Medium 3.5: which to pick
Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 25.5 for Mistral Medium 3.5.
Mistral Medium 3.5 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
- Shared results
- 63
- GLM 5.3 only
- 37
- Mistral Medium 3.5 only
- 18
- 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
GLM 5.3
GLM 5.3 scores 70.6 against 25.5.
6 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 14.2.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3
GLM 5.3 leads, 53.1 against 7.0.
Same board, both measured
Reasoning
GPQA Diamond
GLM 5.3
GLM 5.3 leads, 88.1% against 34.9%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5.3
GLM 5.3 leads, 86.8% against 75.3%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1142.
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
GLM 5.3
GLM 5.3 $0.0036, Mistral Medium 3.5 $0.0053 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
GLM 5.3 $0.36, Mistral Medium 3.5 $6.02.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 70.6 from 6 benchmarks, Mistral Medium 3.5 25.5 from 3 benchmarks.
- Widest category
- Agentic work, 38.0 points apart on average over 5 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GLM 5.3 78.1% against Mistral Medium 3.5 2.9%, 75.2 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $4.40 against Mistral Medium 3.5 $7.50 per million output tokens, 1.7x apart.
- Context window
- GLM 5.3 takes 1.05M 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 5.3 | Bars | Mistral Medium 3.5 | Ties |
|---|---|---|---|---|
| Coding | 12 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 5 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 7 | 0 | ||
| Web design | 8 | 0 | ||
| General | 20 | 0 | ||
| All shared boards | 63 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index120
| Benchmark | GLM 5.3 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| LiveBench coding | 82.8% | Not measured | Only GLM 5.3 measured |
| LMArena WebDev | 1621 | 1264 | GLM 5.3 |
| AA Coding Index | 74.8 | 46.9 | GLM 5.3 |
| LiveCodeBench (Vals AI) | 80.5% | Not measured | Only GLM 5.3 measured |
| SWE-bench Verified (Vals AI) | 95.4% | 66.4% | GLM 5.3 |
| Terminal-Bench 4 (Vals AI) | 38.9% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench (Vals AI) | 78.1% | 2.9% | GLM 5.3 |
| Code migration (Vals AI) | 44.2% | 5.1% | GLM 5.3 |
| 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% | 39.0% | GLM 5.3 |
| 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% | 40.2% | GLM 5.3 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 33.3% | Only Mistral Medium 3.5 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 | 764.0 | GLM 5.3 |
| LMArena Text: coding | 1497 | 1460 | GLM 5.3 |
| LMArena Text: software and IT | 1493 | 1458 | GLM 5.3 |
| LMArena WebDev: React | 1641 | 1254 | GLM 5.3 |
| LMArena WebDev: HTML | 1595 | 1308 | GLM 5.3 |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 14.2 | GLM 5.3 |
| Epoch Capabilities Index | 156.0 | 141.4 | GLM 5.3 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.3 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | 7.0 | 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% | 32.1% | GLM 5.3 |
| Tax Agent Bench (Vals AI) | 39.7% | 4.5% | GLM 5.3 |
| Legal Research Bench (Vals AI) | 49.0% | 9.1% | GLM 5.3 |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | Not measured | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | 56.3% | 11.2% | GLM 5.3 |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only GLM 5.3 measured |
| τ²-Bench Telecom | Not measured | 94.2% | Only Mistral Medium 3.5 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 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 5.3 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | 34.9% | 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 |
| GPQA Diamond (Artificial Analysis run) | 91.7% | 74.8% | GLM 5.3 |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 13.8% | GLM 5.3 |
| 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 | 1436 | GLM 5.3 |
| LMArena Text: expert | 1514 | 1432 | GLM 5.3 |
| LMArena Text: hard prompts in English | 1496 | 1445 | GLM 5.3 |
| 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 | Not measured | 41.4% | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GLM 5.3 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| 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% | 9.0% | GLM 5.3 |
| LMArena Text: math | 1495 | 1431 | GLM 5.3 |
| LMArena Text: mathematical work | 1497 | 1440 | GLM 5.3 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 5.3 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | 75.3% | GLM 5.3 |
| LegalBench (Vals AI) | 84.8% | Not measured | Only GLM 5.3 measured |
| CaseLaw (Vals AI) | Not measured | 44.2% | Only Mistral Medium 3.5 measured |
| CorpFin (Vals AI) | Not measured | 58.8% | Only Mistral Medium 3.5 measured |
| TaxEval (Vals AI) | 72.4% | 68.0% | GLM 5.3 |
| MortgageTax (Vals AI) | Not measured | 28.9% | Only Mistral Medium 3.5 measured |
| MedCode (Vals AI) | 42.9% | 33.8% | GLM 5.3 |
| SimpleQA Verified | 41.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: science | 1498 | 1428 | GLM 5.3 |
| LMArena Text: medicine and healthcare | 1481 | 1409 | GLM 5.3 |
| LMArena Text: legal and government | 1473 | 1437 | GLM 5.3 |
| LMArena Text: business and finance | 1459 | 1426 | GLM 5.3 |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 5.3 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | 1142 | GLM 5.3 |
| Design Arena: UI components | 1314 | 1079 | GLM 5.3 |
| Design Arena: game dev | 1311 | 1079 | GLM 5.3 |
| Design Arena: data viz | 1259 | 1120 | GLM 5.3 |
| Design Arena: 3D | 1332 | 1061 | GLM 5.3 |
| Design Arena: all code categories | 1304 | 1121 | GLM 5.3 |
| Design Arena: SVG | 1293 | 950 | GLM 5.3 |
| Design Arena: ASCII art | 1213 | 1063 | 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 index200
| Benchmark | GLM 5.3 | Mistral Medium 3.5 | 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 | 37.6% | Only Mistral Medium 3.5 measured |
| MedScribe (Vals AI) | 88.8% | 67.7% | GLM 5.3 |
| Vals Multimodal Index | Not measured | 34.8% | Only Mistral Medium 3.5 measured |
| IFBench | Not measured | 68.8% | Only Mistral Medium 3.5 measured |
| Long context reasoning (AA-LCR) | 79.7% | 69.3% | GLM 5.3 |
| DTBench | 87.7% | 75.5% | GLM 5.3 |
| LMCA | 55.5% | 26.1% | GLM 5.3 |
| Surface Evolver Bench | Not measured | 26.9% | Only Mistral Medium 3.5 measured |
| LMArena Text | 1474 | 1421 | GLM 5.3 |
| LMArena Text: instruction following | 1479 | 1415 | GLM 5.3 |
| LMArena Text: creative writing | 1458 | 1374 | GLM 5.3 |
| LMArena Vision | Not measured | 1222 | Only Mistral Medium 3.5 measured |
| LMArena Text: English | 1482 | 1433 | GLM 5.3 |
| LMArena Text: non-English | 1459 | 1404 | GLM 5.3 |
| LMArena Text: Chinese | 1532 | 1444 | GLM 5.3 |
| LMArena Text: German | 1488 | 1453 | GLM 5.3 |
| LMArena Text: Japanese | 1460 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Korean | 1460 | 1384 | GLM 5.3 |
| LMArena Text: French | 1503 | 1448 | GLM 5.3 |
| LMArena Text: Spanish | 1462 | 1411 | GLM 5.3 |
| LMArena Text: Russian | 1469 | 1395 | GLM 5.3 |
| LMArena Text: Polish | 1466 | 1430 | GLM 5.3 |
| LMArena Text: multi-turn | 1472 | 1423 | GLM 5.3 |
| LMArena Text: longer queries | 1483 | 1415 | GLM 5.3 |
| LMArena Text: writing and language | 1469 | 1384 | GLM 5.3 |
| LMArena Text: entertainment and media | 1447 | 1382 | GLM 5.3 |
| 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 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | 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 5.3 costs less
Repository review
50K input + 3K output tokens
GLM 5.3 costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 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 Medium 3.5 |
|---|---|---|
| Maker | Z.ai | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | August 18, 2026 | April 30, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $1.40 | $1.50 |
| Output $/Mtok | $4.40 | $7.50 |
| Coding index | 70.6 | 25.5 |
| Independent measurements behind it | 6 | 3 |
| Cost per solved task | $0.36 | $6.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than Mistral Medium 3.5 for coding?
Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 25.5 for Mistral Medium 3.5. Mistral Medium 3.5 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
Which is cheaper, GLM 5.3 or Mistral Medium 3.5?
GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and Mistral Medium 3.5 $6.02.
Which has the bigger context window, GLM 5.3 or Mistral Medium 3.5?
GLM 5.3, with 1.05M tokens against 262K.
How many benchmarks is the GLM 5.3 vs Mistral Medium 3.5 comparison based on?
63 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 Medium 3.5's on 3. Where only one model has a result, the page says so instead of naming a winner.