Mistral Large 2407 vs Qwen3.5-9B
Qwen3.5-9B leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 13.3 to 7.6, and Qwen3.5-9B costs 40.0x less per output token ($0.15 vs $6.00 per million). Updated October 21, 2026; 9 benchmarks have a result for both.
Mistral Large 2407 or Qwen3.5-9B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mistral Large 2407 for work measured by AA Intelligence Index or τ²-Bench Telecom, where it leads.
- Shared results
- 9
- Mistral Large 2407 only
- 8
- Qwen3.5-9B only
- 6
- Like-for-like categories
- 5 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
Qwen3.5-9B
Qwen3.5-9B leads, 13.3 against 7.6.
Same board, both measured
Long documents
Listed context window
Qwen3.5-9B
Qwen3.5-9B takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-9B
Mistral Large 2407 $0.0050, Qwen3.5-9B $0.0002 per turn.
List prices
At a glance
- Coding index
- Mistral Large 2407 not benchmarked, Qwen3.5-9B not benchmarked.
- Widest category
- Agentic work, 53.8 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Mistral Large 2407 2.0% against Qwen3.5-9B 70.0%, 68.0 points apart on a 0 to 100 scale.
- Output price
- Mistral Large 2407 $6.00 against Qwen3.5-9B $0.15 per million output tokens, 40.0x apart.
- Context window
- Mistral Large 2407 takes 131K tokens, Qwen3.5-9B 262K.
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 2407 | Bars | Qwen3.5-9B | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 1 | ||
| General | 0 | 3 | ||
| All shared boards | 0 | 9 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards, none shared
| Benchmark | Mistral Large 2407 | Qwen3.5-9B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 23.5 | Only Qwen3.5-9B measured |
| LiveCodeBench (Artificial Analysis run) | 26.7% | Not measured | Only Mistral Large 2407 measured |
| SciCode | Not measured | 29.5% | Only Qwen3.5-9B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 24.2% | Only Qwen3.5-9B measured |
| Terminal-Bench | Not measured | 9.2% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Large 2407 | Qwen3.5-9B | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | 13.3 | Qwen3.5-9B |
| Epoch Capabilities Index | Not measured | 139.3 | Only Qwen3.5-9B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Large 2407 | Qwen3.5-9B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.0% | 86.8% | Qwen3.5-9B |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Mistral Large 2407 | Qwen3.5-9B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 47.2% | 80.6% | Qwen3.5-9B |
| Humanity's Last Exam (Artificial Analysis run) | 2.9% | 14.9% | Qwen3.5-9B |
| GPQA Diamond (Epoch AI run) | 49.0% | 79.0% | Qwen3.5-9B |
| SimpleBench | 22.5% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 18 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Large 2407 | Qwen3.5-9B | Leader |
|---|---|---|---|
| AA Math Index | 0.0 | Not measured | Only Mistral Large 2407 measured |
| AIME 2025 (Artificial Analysis run) | 0.0% | Not measured | Only Mistral Large 2407 measured |
| OTIS Mock AIME | 8.5% | 61.7% | Qwen3.5-9B |
| MATH level 5 | 44.8% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Mistral Large 2407 | Qwen3.5-9B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 68.3% | Not measured | Only Mistral Large 2407 measured |
| MMLU | 80.0% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 16 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large 2407 | Qwen3.5-9B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Mistral Large 2407 | Qwen3.5-9B | Leader |
|---|---|---|---|
| IFBench | 31.6% | 66.7% | Qwen3.5-9B |
| Long context reasoning (AA-LCR) | 2.0% | 70.0% | Qwen3.5-9B |
| DTBench | 61.2% | 71.2% | Qwen3.5-9B |
| LMCA | Not measured | 24.5% | Only Qwen3.5-9B measured |
| Creative writing (Lech Mazur) | 69.0% | Not measured | Only Mistral Large 2407 measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large 2407 | Qwen3.5-9B | 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
Qwen3.5-9B costs less
Repository review
50K input + 3K output tokens
Qwen3.5-9B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-9B 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 2407 | Qwen3.5-9B |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | November 19, 2024 | March 10, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $2.00 | $0.10 |
| Output $/Mtok | $6.00 | $0.15 |
| 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 2407 better than Qwen3.5-9B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mistral Large 2407 for work measured by AA Intelligence Index or τ²-Bench Telecom, where it leads.
Which is cheaper, Mistral Large 2407 or Qwen3.5-9B?
Mistral Large 2407 costs $2.00 per million input tokens and $6.00 per million output tokens; Qwen3.5-9B costs $0.10 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large 2407 or Qwen3.5-9B?
Qwen3.5-9B, with 262K tokens against 131K.
How many benchmarks is the Mistral Large 2407 vs Qwen3.5-9B comparison based on?
9 benchmarks have a result for both models as of October 21, 2026. Mistral Large 2407's coding index rests on 0 independent benchmarks and Qwen3.5-9B's on 0. Where only one model has a result, the page says so instead of naming a winner.