Mixtral 8x22B Instruct vs Qwen3.5-9B
Qwen3.5-9B leads Mixtral 8x22B Instruct on the Artificial Analysis Intelligence Index, 13.3 to 5.7, and Qwen3.5-9B costs 40.0x less per output token ($0.15 vs $6.00 per million). Updated October 21, 2026; 5 benchmarks have a result for both.
Mixtral 8x22B Instruct or Qwen3.5-9B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mixtral 8x22B Instruct for work measured by AA Intelligence Index or Epoch Capabilities Index, where it leads.
- Shared results
- 5
- Mixtral 8x22B Instruct only
- 2
- Qwen3.5-9B only
- 10
- 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
Qwen3.5-9B
Qwen3.5-9B leads, 13.3 against 5.7.
Same board, both measured
Long documents
Listed context window
Qwen3.5-9B
Qwen3.5-9B takes 262K tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-9B
Mixtral 8x22B Instruct $0.0050, Qwen3.5-9B $0.0002 per turn.
List prices
At a glance
- Coding index
- Mixtral 8x22B Instruct not benchmarked, Qwen3.5-9B not benchmarked.
- Widest category
- Reasoning, 29.2 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Mixtral 8x22B Instruct 33.2% against Qwen3.5-9B 80.6%, 47.4 points apart on a 0 to 100 scale.
- Output price
- Mixtral 8x22B Instruct $6.00 against Qwen3.5-9B $0.15 per million output tokens, 40.0x apart.
- Context window
- Mixtral 8x22B Instruct takes 66K 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 | Mixtral 8x22B Instruct | Bars | Qwen3.5-9B | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 5 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards, none shared
| Benchmark | Mixtral 8x22B Instruct | Qwen3.5-9B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 23.5 | Only Qwen3.5-9B measured |
| LiveCodeBench (Artificial Analysis run) | 14.8% | Not measured | Only Mixtral 8x22B Instruct 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 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mixtral 8x22B Instruct | Qwen3.5-9B | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.7 | 13.3 | Qwen3.5-9B |
| Epoch Capabilities Index | 122.0 | 139.3 | Qwen3.5-9B |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mixtral 8x22B Instruct | Qwen3.5-9B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 86.8% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mixtral 8x22B Instruct | Qwen3.5-9B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 33.2% | 80.6% | Qwen3.5-9B |
| Humanity's Last Exam (Artificial Analysis run) | 4.0% | 14.9% | Qwen3.5-9B |
| GPQA Diamond (Epoch AI run) | Not measured | 79.0% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mixtral 8x22B Instruct | Qwen3.5-9B | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 61.7% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mixtral 8x22B Instruct | Qwen3.5-9B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 53.7% | Not measured | Only Mixtral 8x22B Instruct 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 | Mixtral 8x22B Instruct | Qwen3.5-9B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mixtral 8x22B Instruct | Qwen3.5-9B | Leader |
|---|---|---|---|
| IFBench | Not measured | 66.7% | Only Qwen3.5-9B measured |
| Long context reasoning (AA-LCR) | Not measured | 70.0% | Only Qwen3.5-9B measured |
| DTBench | 55.1% | 71.2% | Qwen3.5-9B |
| LMCA | Not measured | 24.5% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mixtral 8x22B Instruct | 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 Mixtral 8x22B Instruct | Qwen3.5-9B |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | Open weights | Open weights |
| Released | April 17, 2024 | March 10, 2026 |
| Context window | 66K | 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 Mixtral 8x22B Instruct better than Qwen3.5-9B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mixtral 8x22B Instruct for work measured by AA Intelligence Index or Epoch Capabilities Index, where it leads.
Which is cheaper, Mixtral 8x22B Instruct or Qwen3.5-9B?
Mixtral 8x22B Instruct 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, Mixtral 8x22B Instruct or Qwen3.5-9B?
Qwen3.5-9B, with 262K tokens against 66K.
How many benchmarks is the Mixtral 8x22B Instruct vs Qwen3.5-9B comparison based on?
5 benchmarks have a result for both models as of October 21, 2026. Mixtral 8x22B Instruct'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.