Mistral Large vs Qwen2.5 7B Instruct
Qwen2.5 7B Instruct costs 30.0x less per output token than Mistral Large ($0.20 vs $6.00 per million). Updated October 21, 2026; 3 benchmarks have a result for both.
Mistral Large or Qwen2.5 7B 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 MMLU, where it leads.
- Shared results
- 3
- Mistral Large only
- 41
- Qwen2.5 7B Instruct only
- 4
- Like-for-like categories
- 3 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
No call
Only Mistral Large has a AA Intelligence Index result, 5.8.
Directional only
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 7B Instruct
Mistral Large $0.0050, Qwen2.5 7B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- Mistral Large not benchmarked, Qwen2.5 7B Instruct not benchmarked.
- Widest category
- General, 13.1 points apart on average over 1 shared benchmark.
- Widest single gap
- DTBench: Mistral Large 60.8% against Qwen2.5 7B Instruct 47.7%, 13.1 points apart on a 0 to 100 scale.
- Output price
- Mistral Large $6.00 against Qwen2.5 7B Instruct $0.20 per million output tokens, 30.0x apart.
- Context window
- Mistral Large takes 128K tokens, Qwen2.5 7B 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 7B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Knowledge | 0 | 1 | ||
| General | 1 | 0 | ||
| All shared boards | 2 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | Mistral Large | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | 37.1% | Not measured | Only Mistral Large measured |
| LiveCodeBench (Artificial Analysis run) | 17.8% | Not measured | Only Mistral Large measured |
| LMArena Text: coding | 1275 | Not measured | Only Mistral Large measured |
| LMArena Text: software and IT | 1274 | Not measured | Only Mistral Large measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mistral Large | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.8 | Not measured | Only Mistral Large measured |
| Epoch Capabilities Index | 122.1 | 118.5 | Mistral Large |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mistral Large | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| BALROG | Not measured | 7.8% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Mistral Large | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 47.7% | Not measured | Only Mistral Large measured |
| GPQA Diamond (Artificial Analysis run) | 35.1% | Not measured | Only Mistral Large measured |
| Humanity's Last Exam (Artificial Analysis run) | 3.5% | Not measured | Only Mistral Large measured |
| GPQA Diamond (Epoch AI run) | Not measured | 35.5% | Only Qwen2.5 7B Instruct measured |
| LMArena Text: hard prompts | 1257 | Not measured | Only Mistral Large measured |
| LMArena Text: expert | 1208 | Not measured | Only Mistral Large measured |
| LMArena Text: hard prompts in English | 1268 | Not measured | Only Mistral Large measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Mistral Large | Qwen2.5 7B 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 |
| OTIS Mock AIME | Not measured | 2.5% | Only Qwen2.5 7B Instruct measured |
| LMArena Text: math | 1261 | Not measured | Only Mistral Large measured |
| LMArena Text: mathematical work | 1257 | Not measured | Only Mistral Large measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Large | Qwen2.5 7B 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% | Not measured | Only Mistral Large measured |
| MMLU | 68.8% | 72.9% | Qwen2.5 7B Instruct |
| LMArena Text: science | 1260 | Not measured | Only Mistral Large measured |
| LMArena Text: medicine and healthcare | 1255 | Not measured | Only Mistral Large measured |
| LMArena Text: legal and government | 1275 | Not measured | Only Mistral Large measured |
| LMArena Text: business and finance | 1238 | Not measured | Only Mistral Large measured |
| 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 7B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mistral Large | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| DTBench | 60.8% | 47.7% | Mistral Large |
| LMCA | Not measured | 6.4% | Only Qwen2.5 7B Instruct measured |
| LMArena Text | 1265 | Not measured | Only Mistral Large measured |
| LMArena Text: instruction following | 1249 | Not measured | Only Mistral Large measured |
| LMArena Text: creative writing | 1242 | Not measured | Only Mistral Large measured |
| LMArena Text: English | 1284 | Not measured | Only Mistral Large measured |
| LMArena Text: non-English | 1235 | Not measured | Only Mistral Large measured |
| LMArena Text: Chinese | 1239 | Not measured | Only Mistral Large measured |
| LMArena Text: German | 1242 | Not measured | Only Mistral Large measured |
| LMArena Text: Japanese | 1170 | Not measured | Only Mistral Large measured |
| LMArena Text: Korean | 1202 | Not measured | Only Mistral Large measured |
| LMArena Text: French | 1325 | Not measured | Only Mistral Large measured |
| LMArena Text: Spanish | 1268 | Not measured | Only Mistral Large measured |
| LMArena Text: Russian | 1252 | Not measured | Only Mistral Large measured |
| LMArena Text: Polish | 1157 | Not measured | Only Mistral Large measured |
| LMArena Text: multi-turn | 1260 | Not measured | Only Mistral Large measured |
| LMArena Text: longer queries | 1261 | Not measured | Only Mistral Large measured |
| LMArena Text: writing and language | 1261 | Not measured | Only Mistral Large measured |
| LMArena Text: entertainment and media | 1230 | Not measured | Only Mistral Large measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | Qwen2.5 7B 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 7B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 7B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 7B 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 7B Instruct |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | February 26, 2024 | October 16, 2024 |
| Context window | 128K | 33K |
| Input $/Mtok | $2.00 | $0.10 |
| Output $/Mtok | $6.00 | $0.20 |
| 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 7B 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 MMLU, where it leads.
Which is cheaper, Mistral Large or Qwen2.5 7B Instruct?
Mistral Large costs $2.00 per million input tokens and $6.00 per million output tokens; Qwen2.5 7B Instruct costs $0.10 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large or Qwen2.5 7B Instruct?
Mistral Large, with 128K tokens against 33K.
How many benchmarks is the Mistral Large vs Qwen2.5 7B Instruct comparison based on?
3 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 7B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.