GLM 4.7 Flash vs Mistral Large
GLM 4.7 Flash leads Mistral Large on the Artificial Analysis Intelligence Index, 14.9 to 5.8, and GLM 4.7 Flash costs 15.0x less per output token ($0.40 vs $6.00 per million). Updated October 21, 2026; 30 benchmarks have a result for both.
GLM 4.7 Flash or Mistral Large: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.7 Flash for output tokens 15.0x cheaper ($0.40 vs $6.00 per million) and a larger context window (200K against 128K tokens).
- Shared results
- 30
- GLM 4.7 Flash only
- 13
- Mistral Large only
- 14
- Like-for-like categories
- 6 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.7 Flash
GLM 4.7 Flash leads, 14.9 against 5.8.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Mistral Large has a GPQA Diamond result, 47.7%.
Directional only
Math
AIME
No call
Only Mistral Large has a AIME result, 9.2%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Large has a MMLU Pro result, 69.7%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.7 Flash has a Design Arena: websites result, 1194.
Directional only
Long documents
Listed context window
GLM 4.7 Flash
GLM 4.7 Flash takes 200K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7 Flash
GLM 4.7 Flash $0.0003, Mistral Large $0.0050 per turn.
List prices
At a glance
- Coding index
- GLM 4.7 Flash not benchmarked, Mistral Large not benchmarked.
- Widest category
- Coding, 18.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: GLM 4.7 Flash 1403 against Mistral Large 1239, 27.3 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 Flash $0.40 against Mistral Large $6.00 per million output tokens, 15.0x apart.
- Context window
- GLM 4.7 Flash takes 200K tokens, Mistral Large 128K.
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 Flash | Bars | Mistral Large | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 16 | 0 | ||
| All shared boards | 30 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GLM 4.7 Flash | Mistral Large | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | Not measured | 37.1% | Only Mistral Large measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 17.8% | Only Mistral Large measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: coding | 1382 | 1275 | GLM 4.7 Flash |
| LMArena Text: software and IT | 1389 | 1274 | GLM 4.7 Flash |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 Flash | Mistral Large | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.9 | 5.8 | GLM 4.7 Flash |
| Epoch Capabilities Index | Not measured | 122.1 | Only Mistral Large measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.7 Flash | Mistral Large | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 98.8% | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.7 Flash | Mistral Large | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 47.7% | Only Mistral Large measured |
| GPQA Diamond (Artificial Analysis run) | 58.1% | 35.1% | GLM 4.7 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 7.6% | 3.5% | GLM 4.7 Flash |
| GPQA Diamond (Epoch AI run) | 60.5% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: hard prompts | 1356 | 1257 | GLM 4.7 Flash |
| LMArena Text: expert | 1357 | 1208 | GLM 4.7 Flash |
| LMArena Text: hard prompts in English | 1369 | 1268 | GLM 4.7 Flash |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.7 Flash | Mistral Large | Leader |
|---|---|---|---|
| AIME | Not measured | 9.2% | Only Mistral Large measured |
| MATH 500 (Vals AI) | Not measured | 74.4% | Only Mistral Large measured |
| MGSM (Vals AI) | Not measured | 87.2% | Only Mistral Large measured |
| OTIS Mock AIME | 58.3% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: math | 1355 | 1261 | GLM 4.7 Flash |
| LMArena Text: mathematical work | 1369 | 1257 | GLM 4.7 Flash |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.7 Flash | Mistral Large | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 69.7% | Only Mistral Large measured |
| TaxEval (Vals AI) | Not measured | 63.8% | Only Mistral Large measured |
| MedQA (Vals AI) | Not measured | 76.2% | Only Mistral Large measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 51.5% | Only Mistral Large measured |
| MMLU | Not measured | 68.8% | Only Mistral Large measured |
| LMArena Text: science | 1358 | 1260 | GLM 4.7 Flash |
| LMArena Text: medicine and healthcare | 1343 | 1255 | GLM 4.7 Flash |
| LMArena Text: legal and government | 1357 | 1275 | GLM 4.7 Flash |
| LMArena Text: business and finance | 1343 | 1238 | GLM 4.7 Flash |
| Neither model has a result on the other 9 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.7 Flash | Mistral Large | Leader |
|---|---|---|---|
| Design Arena: websites | 1194 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: UI components | 1208 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: game dev | 1135 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: data viz | 1123 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: 3D | 1121 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: all code categories | 1178 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: SVG | 1038 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index160
| Benchmark | GLM 4.7 Flash | Mistral Large | Leader |
|---|---|---|---|
| IFBench | 60.8% | Not measured | Only GLM 4.7 Flash measured |
| Long context reasoning (AA-LCR) | 41.7% | Not measured | Only GLM 4.7 Flash measured |
| DTBench | Not measured | 60.8% | Only Mistral Large measured |
| LMArena Text | 1351 | 1265 | GLM 4.7 Flash |
| LMArena Text: instruction following | 1327 | 1249 | GLM 4.7 Flash |
| LMArena Text: creative writing | 1296 | 1242 | GLM 4.7 Flash |
| LMArena Text: English | 1373 | 1284 | GLM 4.7 Flash |
| LMArena Text: non-English | 1330 | 1235 | GLM 4.7 Flash |
| LMArena Text: Chinese | 1403 | 1239 | GLM 4.7 Flash |
| LMArena Text: German | 1336 | 1242 | GLM 4.7 Flash |
| LMArena Text: Japanese | Not measured | 1170 | Only Mistral Large measured |
| LMArena Text: Korean | 1283 | 1202 | GLM 4.7 Flash |
| LMArena Text: French | 1332 | 1325 | GLM 4.7 Flash |
| LMArena Text: Spanish | 1350 | 1268 | GLM 4.7 Flash |
| LMArena Text: Russian | 1333 | 1252 | GLM 4.7 Flash |
| LMArena Text: Polish | 1291 | 1157 | GLM 4.7 Flash |
| LMArena Text: multi-turn | 1342 | 1260 | GLM 4.7 Flash |
| LMArena Text: longer queries | 1345 | 1261 | GLM 4.7 Flash |
| LMArena Text: writing and language | 1320 | 1261 | GLM 4.7 Flash |
| LMArena Text: entertainment and media | 1304 | 1230 | GLM 4.7 Flash |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 Flash | Mistral Large | 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 Flash costs less
Repository review
50K input + 3K output tokens
GLM 4.7 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 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 4.7 Flash | Mistral Large |
|---|---|---|
| Maker | Z.ai | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | January 19, 2026 | February 26, 2024 |
| Context window | 200K | 128K |
| Input $/Mtok | $0.0653 | $2.00 |
| Output $/Mtok | $0.40 | $6.00 |
| 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 4.7 Flash better than Mistral Large for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for output tokens 15.0x cheaper ($0.40 vs $6.00 per million) and a larger context window (200K against 128K tokens).
Which is cheaper, GLM 4.7 Flash or Mistral Large?
GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; Mistral Large costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.7 Flash or Mistral Large?
GLM 4.7 Flash, with 200K tokens against 128K.
How many benchmarks is the GLM 4.7 Flash vs Mistral Large comparison based on?
30 benchmarks have a result for both models as of October 21, 2026. GLM 4.7 Flash's coding index rests on 0 independent benchmarks and Mistral Large's on 0. Where only one model has a result, the page says so instead of naming a winner.