Mistral Large vs Qwen2.5 72B Instruct
Qwen2.5 72B Instruct leads Mistral Large on the Artificial Analysis Intelligence Index, 7.7 to 5.8, and Qwen2.5 72B Instruct costs 15.0x less per output token ($0.40 vs $6.00 per million). Updated October 21, 2026; 35 benchmarks have a result for both.
Mistral Large or Qwen2.5 72B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mistral Large for a larger context window (128K against 33K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 35
- Mistral Large only
- 9
- Qwen2.5 72B Instruct only
- 18
- 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
Qwen2.5 72B Instruct
Qwen2.5 72B Instruct leads, 7.7 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
Long documents
Listed context window
Mistral Large
Mistral Large takes 128K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 72B Instruct
Mistral Large $0.0050, Qwen2.5 72B Instruct $0.0006 per turn.
List prices
At a glance
- Coding index
- Mistral Large not benchmarked, Qwen2.5 72B Instruct not benchmarked.
- Widest category
- Knowledge, 7.5 points apart on average over 6 shared benchmarks.
- Widest single gap
- MMLU Pro (Artificial Analysis run): Mistral Large 51.5% against Qwen2.5 72B Instruct 72.0%, 20.5 points apart on a 0 to 100 scale.
- Output price
- Mistral Large $6.00 against Qwen2.5 72B Instruct $0.40 per million output tokens, 15.0x apart.
- Context window
- Mistral Large takes 128K tokens, Qwen2.5 72B Instruct 33K.
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 | Mistral Large | Bars | Qwen2.5 72B Instruct | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 6 | ||
| General | 8 | 9 | ||
| All shared boards | 8 | 27 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Mistral Large | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | 37.1% | Not measured | Only Mistral Large measured |
| LiveCodeBench (Artificial Analysis run) | 17.8% | 27.6% | Qwen2.5 72B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 4.5% | Only Qwen2.5 72B Instruct measured |
| WeirdML | Not measured | 16.0% | Only Qwen2.5 72B Instruct measured |
| LMArena Text: coding | 1275 | 1292 | Qwen2.5 72B Instruct |
| LMArena Text: software and IT | 1274 | 1292 | Qwen2.5 72B Instruct |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.8 | 7.7 | Qwen2.5 72B Instruct |
| Epoch Capabilities Index | 122.1 | 129.0 | Qwen2.5 72B Instruct |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Mistral Large | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 34.5% | Only Qwen2.5 72B Instruct measured |
| METR time horizons | Not measured | 35.8% | Only Qwen2.5 72B Instruct measured |
| BALROG | Not measured | 16.2% | Only Qwen2.5 72B Instruct measured |
| TheAgentCompany | Not measured | 5.7% | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Mistral Large | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 47.7% | Not measured | Only Mistral Large measured |
| GPQA Diamond (Artificial Analysis run) | 35.1% | 49.1% | Qwen2.5 72B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 3.5% | 3.6% | Qwen2.5 72B Instruct |
| GPQA Diamond (Epoch AI run) | Not measured | 49.1% | Only Qwen2.5 72B Instruct measured |
| BIG-Bench Hard | Not measured | 79.8% | Only Qwen2.5 72B Instruct measured |
| LMArena Text: hard prompts | 1257 | 1271 | Qwen2.5 72B Instruct |
| LMArena Text: expert | 1208 | 1245 | Qwen2.5 72B Instruct |
| LMArena Text: hard prompts in English | 1268 | 1279 | Qwen2.5 72B Instruct |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| AIME | 9.2% | Not measured | Only Mistral Large measured |
| MATH 500 (Vals AI) | 74.4% | Not measured | Only Mistral Large measured |
| MGSM (Vals AI) | 87.2% | Not measured | Only Mistral Large measured |
| AA Math Index | Not measured | 14.0 | Only Qwen2.5 72B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 14.0% | Only Qwen2.5 72B Instruct measured |
| OTIS Mock AIME | Not measured | 8.1% | Only Qwen2.5 72B Instruct measured |
| MATH level 5 | Not measured | 63.2% | Only Qwen2.5 72B Instruct measured |
| LMArena Text: math | 1261 | 1283 | Qwen2.5 72B Instruct |
| LMArena Text: mathematical work | 1257 | 1290 | Qwen2.5 72B Instruct |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Mistral Large | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 69.7% | Not measured | Only Mistral Large measured |
| TaxEval (Vals AI) | 63.8% | Not measured | Only Mistral Large measured |
| MedQA (Vals AI) | 76.2% | Not measured | Only Mistral Large measured |
| MMLU Pro (Artificial Analysis run) | 51.5% | 72.0% | Qwen2.5 72B Instruct |
| MMLU | 68.8% | 85.3% | Qwen2.5 72B Instruct |
| AI2 Reasoning Challenge | Not measured | 94.5% | Only Qwen2.5 72B Instruct measured |
| LMArena Text: science | 1260 | 1275 | Qwen2.5 72B Instruct |
| LMArena Text: medicine and healthcare | 1255 | 1258 | Qwen2.5 72B Instruct |
| LMArena Text: legal and government | 1275 | 1288 | Qwen2.5 72B Instruct |
| LMArena Text: business and finance | 1238 | 1253 | Qwen2.5 72B Instruct |
| Neither model has a result on the other 8 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index89
| Benchmark | Mistral Large | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| IFBench | Not measured | 36.9% | Only Qwen2.5 72B Instruct measured |
| DTBench | 60.8% | 62.9% | Qwen2.5 72B Instruct |
| LMCA | Not measured | 13.4% | Only Qwen2.5 72B Instruct measured |
| HellaSwag | Not measured | 84.8% | Only Qwen2.5 72B Instruct measured |
| WinoGrande | Not measured | 82.3% | Only Qwen2.5 72B Instruct measured |
| PIQA | Not measured | 82.6% | Only Qwen2.5 72B Instruct measured |
| LMArena Text | 1265 | 1269 | Qwen2.5 72B Instruct |
| LMArena Text: instruction following | 1249 | 1254 | Qwen2.5 72B Instruct |
| LMArena Text: creative writing | 1242 | 1221 | Mistral Large |
| LMArena Text: English | 1284 | 1283 | Mistral Large |
| LMArena Text: non-English | 1235 | 1252 | Qwen2.5 72B Instruct |
| LMArena Text: Chinese | 1239 | 1273 | Qwen2.5 72B Instruct |
| LMArena Text: German | 1242 | 1234 | Mistral Large |
| LMArena Text: Japanese | 1170 | 1180 | Qwen2.5 72B Instruct |
| LMArena Text: Korean | 1202 | 1188 | Mistral Large |
| LMArena Text: French | 1325 | 1280 | Mistral Large |
| LMArena Text: Spanish | 1268 | 1255 | Mistral Large |
| LMArena Text: Russian | 1252 | 1264 | Qwen2.5 72B Instruct |
| LMArena Text: Polish | 1157 | Not measured | Only Mistral Large measured |
| LMArena Text: multi-turn | 1260 | 1272 | Qwen2.5 72B Instruct |
| LMArena Text: longer queries | 1261 | 1281 | Qwen2.5 72B Instruct |
| LMArena Text: writing and language | 1261 | 1247 | Mistral Large |
| LMArena Text: entertainment and media | 1230 | 1211 | Mistral Large |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | Qwen2.5 72B Instruct | 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
Qwen2.5 72B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 72B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 72B Instruct 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 | Mistral Large | Qwen2.5 72B Instruct |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | February 26, 2024 | September 19, 2024 |
| Context window | 128K | 33K |
| Input $/Mtok | $2.00 | $0.36 |
| Output $/Mtok | $6.00 | $0.40 |
| 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 Mistral Large better than Qwen2.5 72B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mistral Large for a larger context window (128K against 33K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, Mistral Large or Qwen2.5 72B Instruct?
Mistral Large costs $2.00 per million input tokens and $6.00 per million output tokens; Qwen2.5 72B Instruct costs $0.36 and $0.40. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large or Qwen2.5 72B Instruct?
Mistral Large, with 128K tokens against 33K.
How many benchmarks is the Mistral Large vs Qwen2.5 72B Instruct comparison based on?
35 benchmarks have a result for both models as of October 21, 2026. Mistral Large's coding index rests on 0 independent benchmarks and Qwen2.5 72B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.