GLM 5.3 Flash vs Mistral Medium 3.5
Updated October 21, 2026. 69 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 9.2 for Mistral Medium 3.5.
Pick Mistral Medium 3.5 when the benchmark that matters is ALE-Bench, where it leads.
- Shared results
- 69
- GLM 5.3 Flash only
- 33
- Mistral Medium 3.5 only
- 11
- 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, four public coding benchmarks
GLM 5.3 Flash
GLM 5.3 Flash scores 71.0 against 9.2.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 41.8 against 14.2.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 50.9 against 7.0.
Same board, both measured
Reasoning
GPQA Diamond
GLM 5.3 Flash
GLM 5.3 Flash leads, 86.4% against 34.9%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5.3 Flash
GLM 5.3 Flash leads, 86.1% against 75.3%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3 Flash
GLM 5.3 Flash leads, 1272 against 1142.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 Flash has a Vals Index result, 50.9%.
Directional only
Long documents
Listed context window
GLM 5.3 Flash
GLM 5.3 Flash takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 Flash
GLM 5.3 Flash $0.0004, Mistral Medium 3.5 $0.0053 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3 Flash
GLM 5.3 Flash $0.02, Mistral Medium 3.5 $6.02.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 Flash 71.0 from 2 benchmarks, Mistral Medium 3.5 9.2 from 1 benchmark.
- Widest category
- Agentic work, 35.5 points apart on average over 5 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: GLM 5.3 Flash 1624 against Mistral Medium 3.5 1254, 57.7 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 Flash $0.50 against Mistral Medium 3.5 $7.50 per million output tokens, 15.0x apart.
- Context window
- GLM 5.3 Flash 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 Flash | Bars | Mistral Medium 3.5 | Ties |
|---|---|---|---|---|
| Coding | 11 | 1 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 5 | 0 | ||
| Reasoning | 8 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 6 | 0 | ||
| Web design | 8 | 0 | ||
| General | 25 | 0 | ||
| All shared boards | 68 | 1 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index111
| Benchmark | GLM 5.3 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| LiveBench coding | 80.6% | Not measured | Only GLM 5.3 Flash measured |
| LMArena WebDev | 1607 | 1264 | GLM 5.3 Flash |
| AA Coding Index | 71.5 | 46.9 | GLM 5.3 Flash |
| LiveCodeBench (Vals AI) | 80.5% | Not measured | Only GLM 5.3 Flash measured |
| SWE-bench Verified (Vals AI) | 92.0% | 66.4% | GLM 5.3 Flash |
| Terminal-Bench 4 (Vals AI) | 37.4% | Not measured | Only GLM 5.3 Flash measured |
| Vibe Code Bench (Vals AI) | 30.8% | 2.9% | GLM 5.3 Flash |
| Code migration (Vals AI) | 20.5% | 5.1% | GLM 5.3 Flash |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only GLM 5.3 Flash measured |
| IOI (Vals AI) | 52.5% | Not measured | Only GLM 5.3 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 62.9% | 39.0% | GLM 5.3 Flash |
| Vibe Code Bench 1-100 (Vals AI) | 16.0% | Not measured | Only GLM 5.3 Flash measured |
| SkillsBench (Vals AI) | 40.2% | Not measured | Only GLM 5.3 Flash measured |
| CyberBench (Vals AI) | 67.9% | Not measured | Only GLM 5.3 Flash measured |
| SciCode | 51.6% | 40.2% | GLM 5.3 Flash |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 33.3% | Only Mistral Medium 3.5 measured |
| FrontierCode | 31.8% | Not measured | Only GLM 5.3 Flash measured |
| FrontierSWE | 18.1% | Not measured | Only GLM 5.3 Flash measured |
| ALE-Bench | 303.6 | 764.0 | Mistral Medium 3.5 |
| LMArena Text: coding | 1507 | 1460 | GLM 5.3 Flash |
| LMArena Text: software and IT | 1500 | 1458 | GLM 5.3 Flash |
| LMArena WebDev: React | 1624 | 1254 | GLM 5.3 Flash |
| LMArena WebDev: HTML | 1577 | 1308 | GLM 5.3 Flash |
| LMArena WebDev: from an image | 1578 | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 41.8 | 14.2 | GLM 5.3 Flash |
| Epoch Capabilities Index | 153.1 | 141.4 | GLM 5.3 Flash |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.3 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Agentic Index | 50.9 | 7.0 | GLM 5.3 Flash |
| LiveBench research | 70.1% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench browser | 84.0% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench long agentic | 28.4% | Not measured | Only GLM 5.3 Flash measured |
| SREBench (Vals AI) | 0.8% | Not measured | Only GLM 5.3 Flash measured |
| Finance Agent (Vals AI) | 57.9% | 32.1% | GLM 5.3 Flash |
| Tax Agent Bench (Vals AI) | 31.6% | 4.5% | GLM 5.3 Flash |
| Legal Research Bench (Vals AI) | 45.2% | 9.1% | GLM 5.3 Flash |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.4% | Not measured | Only GLM 5.3 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 55.9% | 11.2% | GLM 5.3 Flash |
| Terminal-Bench-Science (Vals AI) | 2.9% | Not measured | Only GLM 5.3 Flash measured |
| τ²-Bench Telecom | Not measured | 94.2% | Only Mistral Medium 3.5 measured |
| APEX-Agents | 52.8% | Not measured | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 5.3 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 86.4% | 34.9% | GLM 5.3 Flash |
| LiveBench reasoning | 77.6% | Not measured | Only GLM 5.3 Flash measured |
| MMMU Pro (Vals AI) | 86.0% | Not measured | Only GLM 5.3 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 91.2% | 74.8% | GLM 5.3 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 39.9% | 13.8% | GLM 5.3 Flash |
| GPQA Diamond (Epoch AI run) | 90.2% | Not measured | Only GLM 5.3 Flash measured |
| ARC-AGI | 91.0% | Not measured | Only GLM 5.3 Flash measured |
| ARC-AGI-2 | 65.8% | Not measured | Only GLM 5.3 Flash measured |
| Chess puzzles | 21.0% | Not measured | Only GLM 5.3 Flash measured |
| Mystery game puzzles | 8.0% | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts | 1490 | 1436 | GLM 5.3 Flash |
| LMArena Text: expert | 1511 | 1432 | GLM 5.3 Flash |
| LMArena Text: hard prompts in English | 1488 | 1445 | GLM 5.3 Flash |
| LMArena Vision: diagrams | 1317 | 1254 | GLM 5.3 Flash |
| LMArena Vision: homework | 1318 | 1230 | GLM 5.3 Flash |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GLM 5.3 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 93.9% | Not measured | Only GLM 5.3 Flash measured |
| FrontierMath, tiers 1 to 3 | 55.8% | Not measured | Only GLM 5.3 Flash measured |
| FrontierMath, tier 4 | 17.1% | Not measured | Only GLM 5.3 Flash measured |
| ProofBench | 21.0% | 9.0% | GLM 5.3 Flash |
| LMArena Text: math | 1503 | 1431 | GLM 5.3 Flash |
| LMArena Text: mathematical work | 1497 | 1440 | GLM 5.3 Flash |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 5.3 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| MMLU Pro | 86.1% | 75.3% | GLM 5.3 Flash |
| LegalBench (Vals AI) | 83.9% | Not measured | Only GLM 5.3 Flash 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) | 75.6% | 68.0% | GLM 5.3 Flash |
| MortgageTax (Vals AI) | Not measured | 28.9% | Only Mistral Medium 3.5 measured |
| MedCode (Vals AI) | Not measured | 33.8% | Only Mistral Medium 3.5 measured |
| LMArena Text: science | 1484 | 1428 | GLM 5.3 Flash |
| LMArena Text: medicine and healthcare | 1475 | 1409 | GLM 5.3 Flash |
| LMArena Text: legal and government | 1473 | 1437 | GLM 5.3 Flash |
| LMArena Text: business and finance | 1460 | 1426 | GLM 5.3 Flash |
| 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 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1272 | 1142 | GLM 5.3 Flash |
| Design Arena: UI components | 1316 | 1079 | GLM 5.3 Flash |
| Design Arena: game dev | 1279 | 1079 | GLM 5.3 Flash |
| Design Arena: data viz | 1262 | 1120 | GLM 5.3 Flash |
| Design Arena: 3D | 1316 | 1061 | GLM 5.3 Flash |
| Design Arena: all code categories | 1282 | 1122 | GLM 5.3 Flash |
| Design Arena: SVG | 1284 | 949 | GLM 5.3 Flash |
| Design Arena: ASCII art | 1269 | 1062 | GLM 5.3 Flash |
| LiveBench design | 77.2% | Not measured | Only GLM 5.3 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index250
| Benchmark | GLM 5.3 Flash | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Vals Index | 50.9% | Not measured | Only GLM 5.3 Flash measured |
| LiveBench data analysis | 76.4% | Not measured | Only GLM 5.3 Flash measured |
| SAGE (Vals AI) | Not measured | 37.6% | Only Mistral Medium 3.5 measured |
| MedScribe (Vals AI) | 88.9% | 67.7% | GLM 5.3 Flash |
| 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) | 80.0% | 69.3% | GLM 5.3 Flash |
| DTBench | Not measured | 75.5% | Only Mistral Medium 3.5 measured |
| LMCA | Not measured | 26.1% | Only Mistral Medium 3.5 measured |
| Surface Evolver Bench | 52.5% | 26.9% | GLM 5.3 Flash |
| LMArena Text | 1471 | 1421 | GLM 5.3 Flash |
| LMArena Text: instruction following | 1478 | 1415 | GLM 5.3 Flash |
| LMArena Text: creative writing | 1441 | 1374 | GLM 5.3 Flash |
| LMArena Vision | 1294 | 1222 | GLM 5.3 Flash |
| LMArena Text: English | 1474 | 1433 | GLM 5.3 Flash |
| LMArena Text: non-English | 1462 | 1404 | GLM 5.3 Flash |
| LMArena Text: Chinese | 1531 | 1444 | GLM 5.3 Flash |
| LMArena Text: German | 1462 | 1453 | GLM 5.3 Flash |
| LMArena Text: Japanese | 1444 | Not measured | Only GLM 5.3 Flash measured |
| LMArena Text: Korean | 1436 | 1384 | GLM 5.3 Flash |
| LMArena Text: French | 1491 | 1448 | GLM 5.3 Flash |
| LMArena Text: Spanish | 1475 | 1411 | GLM 5.3 Flash |
| LMArena Text: Russian | 1465 | 1395 | GLM 5.3 Flash |
| LMArena Text: Polish | 1450 | 1430 | GLM 5.3 Flash |
| LMArena Text: multi-turn | 1469 | 1423 | GLM 5.3 Flash |
| LMArena Text: longer queries | 1481 | 1415 | GLM 5.3 Flash |
| LMArena Text: writing and language | 1455 | 1384 | GLM 5.3 Flash |
| LMArena Text: entertainment and media | 1440 | 1382 | GLM 5.3 Flash |
| LMArena Vision: English | 1295 | 1240 | GLM 5.3 Flash |
| LMArena Vision: Chinese | 1363 | 1244 | GLM 5.3 Flash |
| LMArena Vision: reading text | 1299 | 1233 | GLM 5.3 Flash |
| LMArena Vision: humor | 1279 | 1216 | GLM 5.3 Flash |
| LMArena Vision: creative writing | 1270 | 1198 | GLM 5.3 Flash |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 Flash | 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 Flash costs less
Repository review
50K input + 3K output tokens
GLM 5.3 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 Flash 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 Flash | Mistral Medium 3.5 |
|---|---|---|
| Maker | Z.ai | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | August 26, 2026 | April 30, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.15 | $1.50 |
| Output $/Mtok | $0.50 | $7.50 |
| Coding index | 71.0 | 9.2 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.02 | $6.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 Flash better than Mistral Medium 3.5 for coding?
Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 9.2 for Mistral Medium 3.5. Pick Mistral Medium 3.5 when the benchmark that matters is ALE-Bench, where it leads.
Which is cheaper, GLM 5.3 Flash or Mistral Medium 3.5?
GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Per solved SWE-bench Verified task, GLM 5.3 Flash costs $0.02 and Mistral Medium 3.5 $6.02.
Which has the bigger context window, GLM 5.3 Flash or Mistral Medium 3.5?
GLM 5.3 Flash, with 1.05M tokens against 262K.
How many benchmarks is the GLM 5.3 Flash vs Mistral Medium 3.5 comparison based on?
69 benchmarks have a result for both models as of October 21, 2026. GLM 5.3 Flash's coding index rests on 2 independent benchmarks and Mistral Medium 3.5's on 1. Where only one model has a result, the page says so instead of naming a winner.