GLM 4.7 vs Mistral Large 4
Mistral Large 4 leads GLM 4.7 on coding, 56.4 to 55.1 on the Vibe Coding Index, and GLM 4.7 costs 1.9x less per output token ($2.20 vs $4.18 per million). Updated October 22, 2026; 48 benchmarks have a result for both.
GLM 4.7 or Mistral Large 4: which to pick
GLM 4.7 and Mistral Large 4 are level for coding, 55.1 against 56.4 on the Vibe Coding Index, so price and context decide.
Pick GLM 4.7 for output tokens 1.9x cheaper ($2.20 vs $4.18 per million) and work measured by LMArena Text: hard prompts in English or SimpleQA Verified, where it leads. Pick Mistral Large 4 for a larger context window (1.05M against 205K tokens) and work measured by LMArena WebDev or ALE-Bench, where it leads.
- Shared results
- 48
- GLM 4.7 only
- 33
- Mistral Large 4 only
- 33
- Like-for-like categories
- 7 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
Level: 55.1 against 56.4, inside 2 points.
2 and 5 benchmarks behind the scores
Intelligence
AA Intelligence Index
Mistral Large 4
Mistral Large 4 leads, 38.4 against 22.2.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 4.7 has a GPQA Diamond result, 80.0%.
Directional only
Math
AIME
No call
Only GLM 4.7 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.7 has a MMLU Pro result, 82.7%.
Directional only
Web design
Design Arena: websites
Mistral Large 4
Mistral Large 4 leads, 1257 against 1225.
Same board, both measured
Long documents
Listed context window
Mistral Large 4
Mistral Large 4 takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7
GLM 4.7 $0.0017, Mistral Large 4 $0.0034 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 4.7 has the number, $0.29.
Directional only
At a glance
- Coding index
- GLM 4.7 55.1 from 2 benchmarks, Mistral Large 4 56.4 from 5 benchmarks.
- Widest category
- Coding, 13.1 points apart on average over 6 shared benchmarks.
- Widest single gap
- ALE-Bench: GLM 4.7 399.5 against Mistral Large 4 1213.2, 27.1 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $2.20 against Mistral Large 4 $4.18 per million output tokens, 1.9x apart.
- Context window
- GLM 4.7 takes 205K tokens, Mistral Large 4 1.05M.
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 4.7 | Bars | Mistral Large 4 | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 1 | 5 | ||
| Math | 0 | 4 | ||
| Knowledge | 4 | 2 | ||
| Web design | 0 | 8 | ||
| General | 10 | 6 | ||
| All shared boards | 15 | 33 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
| Benchmark | GLM 4.7 | Mistral Large 4 | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 60.0% | Only Mistral Large 4 measured |
| LMArena WebDev | 1435 | 1543 | Mistral Large 4 |
| AA Coding Index | 45.3 | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Vals AI) | 82.2% | Not measured | Only GLM 4.7 measured |
| SWE-bench Verified (Vals AI) | 69.4% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 27.8% | Only Mistral Large 4 measured |
| Vibe Code Bench (Vals AI) | Not measured | 78.4% | Only Mistral Large 4 measured |
| Code migration (Vals AI) | Not measured | 30.6% | Only Mistral Large 4 measured |
| IOI (Vals AI) | Not measured | 45.3% | Only Mistral Large 4 measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 33.1% | Only Mistral Large 4 measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 14.3% | Only Mistral Large 4 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GLM 4.7 measured |
| SciCode | Not measured | 54.2% | Only Mistral Large 4 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| FrontierCode | Not measured | 39.4% | Only Mistral Large 4 measured |
| ALE-Bench | 399.5 | 1213.2 | Mistral Large 4 |
| LMArena Text: coding | 1454 | 1478 | Mistral Large 4 |
| LMArena Text: software and IT | 1453 | 1467 | Mistral Large 4 |
| LMArena WebDev: React | 1420 | 1559 | Mistral Large 4 |
| LMArena WebDev: HTML | 1447 | 1487 | Mistral Large 4 |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.7 | Mistral Large 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 38.4 | Mistral Large 4 |
| Epoch Capabilities Index | 143.6 | 150.0 | Mistral Large 4 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index16 boards, none shared
| Benchmark | GLM 4.7 | Mistral Large 4 | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1050 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | 1105 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Android apps | 1112 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | 1058 | Not measured | Only GLM 4.7 measured |
| LiveBench research | Not measured | 28.1% | Only Mistral Large 4 measured |
| LiveBench browser | Not measured | 8.0% | Only Mistral Large 4 measured |
| LiveBench long agentic | Not measured | 11.8% | Only Mistral Large 4 measured |
| Finance Agent (Vals AI) | Not measured | 54.7% | Only Mistral Large 4 measured |
| Tax Agent Bench (Vals AI) | Not measured | 30.4% | Only Mistral Large 4 measured |
| Legal Research Bench (Vals AI) | Not measured | 31.7% | Only Mistral Large 4 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 17.5% | Only Mistral Large 4 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 56.2% | Only Mistral Large 4 measured |
| BioMysteryBench (Vals AI) | Not measured | 67.0% | Only Mistral Large 4 measured |
| τ²-Bench Telecom | 95.9% | Not measured | Only GLM 4.7 measured |
| Vending-Bench 2 | $2,377 | Not measured | Only GLM 4.7 measured |
| BrowseComp-Plus | 64.7% | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | GLM 4.7 | Mistral Large 4 | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | Not measured | Only GLM 4.7 measured |
| LiveBench reasoning | Not measured | 83.9% | Only Mistral Large 4 measured |
| MysteryMechanism (Vals AI) | Not measured | 12.2% | Only Mistral Large 4 measured |
| GPQA Diamond (Artificial Analysis run) | 85.9% | Not measured | Only GLM 4.7 measured |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 35.0% | Mistral Large 4 |
| GPQA Diamond (Epoch AI run) | 83.3% | 88.1% | Mistral Large 4 |
| SimpleBench | 47.7% | Not measured | Only GLM 4.7 measured |
| Chess puzzles | 21.0% | 33.0% | Mistral Large 4 |
| Mystery game puzzles | Not measured | 17.0% | Only Mistral Large 4 measured |
| LMArena Text: hard prompts | 1443 | 1444 | Mistral Large 4 |
| LMArena Text: expert | 1424 | 1444 | Mistral Large 4 |
| LMArena Text: hard prompts in English | 1452 | 1442 | GLM 4.7 |
| LMArena Vision: diagrams | Not measured | 1288 | Only Mistral Large 4 measured |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 4.7 | Mistral Large 4 | Leader |
|---|---|---|---|
| AIME | 93.3% | Not measured | Only GLM 4.7 measured |
| MGSM (Vals AI) | 88.2% | Not measured | Only GLM 4.7 measured |
| AA Math Index | 95.0 | Not measured | Only GLM 4.7 measured |
| AIME 2025 (Artificial Analysis run) | 95.0% | Not measured | Only GLM 4.7 measured |
| OTIS Mock AIME | 83.3% | 98.3% | Mistral Large 4 |
| FrontierMath, tiers 1 to 3 | Not measured | 58.6% | Only Mistral Large 4 measured |
| FrontierMath, tier 4 | Not measured | 26.8% | Only Mistral Large 4 measured |
| ProofBench | 6.0% | 10.0% | Mistral Large 4 |
| LMArena Text: math | 1424 | 1469 | Mistral Large 4 |
| LMArena Text: mathematical work | 1411 | 1462 | Mistral Large 4 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index42
| Benchmark | GLM 4.7 | Mistral Large 4 | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | Not measured | Only GLM 4.7 measured |
| LegalBench (Vals AI) | 83.4% | Not measured | Only GLM 4.7 measured |
| CaseLaw (Vals AI) | 54.9% | Not measured | Only GLM 4.7 measured |
| CorpFin (Vals AI) | 46.4% | Not measured | Only GLM 4.7 measured |
| TaxEval (Vals AI) | 68.8% | Not measured | Only GLM 4.7 measured |
| MedQA (Vals AI) | 93.7% | Not measured | Only GLM 4.7 measured |
| MedCode (Vals AI) | 32.8% | 40.7% | Mistral Large 4 |
| MMLU Pro (Artificial Analysis run) | 85.6% | Not measured | Only GLM 4.7 measured |
| SimpleQA Verified | 32.2% | 20.0% | GLM 4.7 |
| LMArena Text: science | 1457 | 1448 | GLM 4.7 |
| LMArena Text: medicine and healthcare | 1469 | 1433 | GLM 4.7 |
| LMArena Text: legal and government | 1444 | 1425 | GLM 4.7 |
| LMArena Text: business and finance | 1429 | 1439 | Mistral Large 4 |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | GLM 4.7 | Mistral Large 4 | Leader |
|---|---|---|---|
| Design Arena: websites | 1225 | 1257 | Mistral Large 4 |
| Design Arena: UI components | 1196 | 1272 | Mistral Large 4 |
| Design Arena: game dev | 1188 | 1225 | Mistral Large 4 |
| Design Arena: data viz | 1195 | 1275 | Mistral Large 4 |
| Design Arena: 3D | 1190 | 1252 | Mistral Large 4 |
| Design Arena: all code categories | 1216 | 1259 | Mistral Large 4 |
| Design Arena: SVG | 1142 | 1232 | Mistral Large 4 |
| Design Arena: ASCII art | 1165 | 1202 | Mistral Large 4 |
| LiveBench design | Not measured | 65.6% | Only Mistral Large 4 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index106
| Benchmark | GLM 4.7 | Mistral Large 4 | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.5% | Only Mistral Large 4 measured |
| MedScribe (Vals AI) | 68.6% | 80.4% | Mistral Large 4 |
| IFBench | 67.9% | Not measured | Only GLM 4.7 measured |
| Long context reasoning (AA-LCR) | 71.0% | 81.3% | Mistral Large 4 |
| DTBench | Not measured | 88.2% | Only Mistral Large 4 measured |
| LMCA | Not measured | 35.8% | Only Mistral Large 4 measured |
| CL-bench | 15.9% | Not measured | Only GLM 4.7 measured |
| Surface Evolver Bench | Not measured | 28.1% | Only Mistral Large 4 measured |
| Furniture assembly | Not measured | 20.8% | Only Mistral Large 4 measured |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | 1431 | GLM 4.7 |
| LMArena Text: instruction following | 1412 | 1422 | Mistral Large 4 |
| LMArena Text: creative writing | 1401 | 1363 | GLM 4.7 |
| LMArena Vision | Not measured | 1272 | Only Mistral Large 4 measured |
| LMArena Text: English | 1453 | 1433 | GLM 4.7 |
| LMArena Text: non-English | 1417 | 1422 | Mistral Large 4 |
| LMArena Text: Chinese | 1495 | 1492 | GLM 4.7 |
| LMArena Text: German | 1426 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Japanese | 1439 | 1415 | GLM 4.7 |
| LMArena Text: Korean | 1399 | Not measured | Only GLM 4.7 measured |
| LMArena Text: French | 1432 | 1492 | Mistral Large 4 |
| LMArena Text: Spanish | 1434 | 1455 | Mistral Large 4 |
| LMArena Text: Russian | 1423 | 1417 | GLM 4.7 |
| LMArena Text: Polish | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: multi-turn | 1446 | 1435 | GLM 4.7 |
| LMArena Text: longer queries | 1432 | 1428 | GLM 4.7 |
| LMArena Text: writing and language | 1411 | 1386 | GLM 4.7 |
| LMArena Text: entertainment and media | 1400 | 1378 | GLM 4.7 |
| LMArena Vision: English | Not measured | 1283 | Only Mistral Large 4 measured |
| LMArena Vision: reading text | Not measured | 1284 | Only Mistral Large 4 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 4.7 | Mistral Large 4 | 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 4.7 costs less
Repository review
50K input + 3K output tokens
GLM 4.7 costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 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 4.7 | Mistral Large 4 |
|---|---|---|
| Maker | Z.ai | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | December 22, 2025 | October 6, 2026 |
| Context window | 205K | 1.05M |
| Input $/Mtok | $0.60 | $1.36 |
| Output $/Mtok | $2.20 | $4.18 |
| Coding index | 55.1 | 56.4 |
| Independent measurements behind it | 2 | 5 |
| Cost per solved task | $0.29 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than Mistral Large 4 for coding?
GLM 4.7 and Mistral Large 4 are level for coding, 55.1 against 56.4 on the Vibe Coding Index, so price and context decide. Pick GLM 4.7 for output tokens 1.9x cheaper ($2.20 vs $4.18 per million) and work measured by LMArena Text: hard prompts in English or SimpleQA Verified, where it leads. Pick Mistral Large 4 for a larger context window (1.05M against 205K tokens) and work measured by LMArena WebDev or ALE-Bench, where it leads.
Which is cheaper, GLM 4.7 or Mistral Large 4?
GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Mistral Large 4 costs $1.36 and $4.18. Cost per solved task is known for GLM 4.7 only.
Which has the bigger context window, GLM 4.7 or Mistral Large 4?
Mistral Large 4, with 1.05M tokens against 205K.
How many benchmarks is the GLM 4.7 vs Mistral Large 4 comparison based on?
48 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and Mistral Large 4's on 5. Where only one model has a result, the page says so instead of naming a winner.