GLM 4.5 vs Mistral Large
Updated October 21, 2026. 41 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
GLM 4.5 has the edge where output tokens cost 2.7x less, and where the context window is larger, 131K against 128K tokens.
- Shared results
- 41
- GLM 4.5 only
- 19
- Mistral Large only
- 3
- Like-for-like categories
- 6 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, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.5
GLM 4.5 leads, 12.8 against 5.8.
Same board, both measured
Reasoning
GPQA Diamond
GLM 4.5
GLM 4.5 leads, 72.2% against 47.7%.
Same board, both measured
Math
AIME
GLM 4.5
GLM 4.5 leads, 86.7% against 9.2%.
Same board, both measured
Knowledge
MMLU Pro
GLM 4.5
GLM 4.5 leads, 81.2% against 69.7%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 4.5 has a Design Arena: websites result, 1170.
Directional only
Long documents
Listed context window
GLM 4.5
GLM 4.5 takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5
GLM 4.5 $0.0017, Mistral Large $0.0050 per turn.
List prices
At a glance
- Coding index
- GLM 4.5 not benchmarked, Mistral Large not benchmarked.
- Widest category
- Coding, 35.1 points apart on average over 4 shared benchmarks.
- Widest single gap
- AIME: GLM 4.5 86.7% against Mistral Large 9.2%, 77.5 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 $2.20 against Mistral Large $6.00 per million output tokens, 2.7x apart.
- Context window
- GLM 4.5 takes 131K 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 4.5 | Bars | Mistral Large | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 5 | 0 | ||
| Knowledge | 8 | 0 | ||
| General | 17 | 0 | ||
| All shared boards | 41 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GLM 4.5 | Mistral Large | Leader |
|---|---|---|---|
| SWE-bench Verified | 64.2% | Not measured | Only GLM 4.5 measured |
| LiveCodeBench (Vals AI) | 67.5% | 37.1% | GLM 4.5 |
| LiveCodeBench (Artificial Analysis run) | 73.8% | 17.8% | GLM 4.5 |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | Not measured | Only GLM 4.5 measured |
| WeirdML | 40.6% | Not measured | Only GLM 4.5 measured |
| ALE-Bench | 344.8 | Not measured | Only GLM 4.5 measured |
| LMArena Text: coding | 1434 | 1275 | GLM 4.5 |
| LMArena Text: software and IT | 1440 | 1274 | GLM 4.5 |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.5 | Mistral Large | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.8 | 5.8 | GLM 4.5 |
| 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 4.5 | Mistral Large | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 43.0% | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 4.5 | Mistral Large | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | 47.7% | GLM 4.5 |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 35.1% | GLM 4.5 |
| Humanity's Last Exam (Artificial Analysis run) | 13.0% | 3.5% | GLM 4.5 |
| LMArena Text: hard prompts | 1429 | 1257 | GLM 4.5 |
| LMArena Text: expert | 1433 | 1208 | GLM 4.5 |
| LMArena Text: hard prompts in English | 1429 | 1268 | GLM 4.5 |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.5 | Mistral Large | Leader |
|---|---|---|---|
| AIME | 86.7% | 9.2% | GLM 4.5 |
| MATH 500 (Vals AI) | 94.0% | 74.4% | GLM 4.5 |
| MGSM (Vals AI) | 90.8% | 87.2% | GLM 4.5 |
| 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 |
| LMArena Text: math | 1427 | 1261 | GLM 4.5 |
| LMArena Text: mathematical work | 1430 | 1257 | GLM 4.5 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 4.5 | Mistral Large | Leader |
|---|---|---|---|
| MMLU Pro | 81.2% | 69.7% | GLM 4.5 |
| LegalBench (Vals AI) | 75.6% | Not measured | Only GLM 4.5 measured |
| CorpFin (Vals AI) | 61.0% | Not measured | Only GLM 4.5 measured |
| TaxEval (Vals AI) | 72.4% | 63.8% | GLM 4.5 |
| MedQA (Vals AI) | 90.0% | 76.2% | GLM 4.5 |
| MMLU Pro (Artificial Analysis run) | 83.5% | 51.5% | GLM 4.5 |
| MMLU | Not measured | 68.8% | Only Mistral Large measured |
| LMArena Text: science | 1444 | 1260 | GLM 4.5 |
| LMArena Text: medicine and healthcare | 1455 | 1255 | GLM 4.5 |
| LMArena Text: legal and government | 1438 | 1275 | GLM 4.5 |
| LMArena Text: business and finance | 1420 | 1238 | GLM 4.5 |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.5 | Mistral Large | Leader |
|---|---|---|---|
| Design Arena: websites | 1170 | Not measured | Only GLM 4.5 measured |
| Design Arena: UI components | 1144 | Not measured | Only GLM 4.5 measured |
| Design Arena: game dev | 1145 | Not measured | Only GLM 4.5 measured |
| Design Arena: data viz | 1159 | Not measured | Only GLM 4.5 measured |
| Design Arena: 3D | 1170 | Not measured | Only GLM 4.5 measured |
| Design Arena: all code categories | 1165 | Not measured | Only GLM 4.5 measured |
| Design Arena: SVG | 1096 | Not measured | Only GLM 4.5 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
| Benchmark | GLM 4.5 | Mistral Large | Leader |
|---|---|---|---|
| IFBench | 44.1% | Not measured | Only GLM 4.5 measured |
| Long context reasoning (AA-LCR) | 52.7% | Not measured | Only GLM 4.5 measured |
| DTBench | Not measured | 60.8% | Only Mistral Large measured |
| Creative writing (Lech Mazur) | 73.4% | Not measured | Only GLM 4.5 measured |
| LMArena Text | 1430 | 1265 | GLM 4.5 |
| LMArena Text: instruction following | 1404 | 1249 | GLM 4.5 |
| LMArena Text: creative writing | 1395 | 1242 | GLM 4.5 |
| LMArena Text: English | 1437 | 1284 | GLM 4.5 |
| LMArena Text: non-English | 1417 | 1235 | GLM 4.5 |
| LMArena Text: Chinese | 1465 | 1239 | GLM 4.5 |
| LMArena Text: German | 1407 | 1242 | GLM 4.5 |
| LMArena Text: Japanese | 1415 | 1170 | GLM 4.5 |
| LMArena Text: Korean | 1380 | 1202 | GLM 4.5 |
| LMArena Text: French | 1418 | 1325 | GLM 4.5 |
| LMArena Text: Spanish | 1454 | 1268 | GLM 4.5 |
| LMArena Text: Russian | 1413 | 1252 | GLM 4.5 |
| LMArena Text: Polish | 1422 | 1157 | GLM 4.5 |
| LMArena Text: multi-turn | 1415 | 1260 | GLM 4.5 |
| LMArena Text: longer queries | 1412 | 1261 | GLM 4.5 |
| LMArena Text: writing and language | 1403 | 1261 | GLM 4.5 |
| LMArena Text: entertainment and media | 1404 | 1230 | GLM 4.5 |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 | 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 4.5 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 Large |
|---|---|---|
| Maker | Z.ai | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | July 25, 2025 | February 26, 2024 |
| Context window | 131K | 128K |
| Input $/Mtok | $0.60 | $2.00 |
| Output $/Mtok | $2.20 | $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 4.5 better than Mistral Large for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.5 has the edge where output tokens cost 2.7x less, and where the context window is larger, 131K against 128K tokens.
Which is cheaper, GLM 4.5 or Mistral Large?
GLM 4.5 costs $0.60 per million input tokens and $2.20 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 4.5 or Mistral Large?
GLM 4.5, with 131K tokens against 128K.
How many benchmarks is the GLM 4.5 vs Mistral Large comparison based on?
41 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 Large's on 0. Where only one model has a result, the page says so instead of naming a winner.