Mixtral 8x22B Instruct vs Qwen3 14B
Qwen3 14B leads Mixtral 8x22B Instruct on the Artificial Analysis Intelligence Index, 6.7 to 5.7, and Qwen3 14B costs 26.1x less per output token ($0.23 vs $6.00 per million). Updated October 21, 2026; 7 benchmarks have a result for both.
Mixtral 8x22B Instruct or Qwen3 14B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mixtral 8x22B Instruct for a larger context window (66K against 41K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or AA Intelligence Index, where it leads.
- Shared results
- 7
- Mixtral 8x22B Instruct only
- 0
- Qwen3 14B only
- 11
- 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 14B
Qwen3 14B leads, 6.7 against 5.7.
Same board, both measured
Long documents
Listed context window
Mixtral 8x22B Instruct
Mixtral 8x22B Instruct takes 66K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 14B
Mixtral 8x22B Instruct $0.0050, Qwen3 14B $0.0002 per turn.
List prices
At a glance
- Coding index
- Mixtral 8x22B Instruct not benchmarked, Qwen3 14B not benchmarked.
- Widest category
- Coding, 37.5 points apart on average over 1 shared benchmark.
- Widest single gap
- LiveCodeBench (Artificial Analysis run): Mixtral 8x22B Instruct 14.8% against Qwen3 14B 52.3%, 37.5 points apart on a 0 to 100 scale.
- Output price
- Mixtral 8x22B Instruct $6.00 against Qwen3 14B $0.23 per million output tokens, 26.1x apart.
- Context window
- Mixtral 8x22B Instruct takes 66K tokens, Qwen3 14B 41K.
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 14B | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 2 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Mixtral 8x22B Instruct | Qwen3 14B | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 14.8% | 52.3% | Qwen3 14B |
| SciCode | Not measured | 30.7% | Only Qwen3 14B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 5.3% | Only Qwen3 14B measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mixtral 8x22B Instruct | Qwen3 14B | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.7 | 6.7 | Qwen3 14B |
| Epoch Capabilities Index | 122.0 | 138.2 | Qwen3 14B |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mixtral 8x22B Instruct | Qwen3 14B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 34.5% | Only Qwen3 14B 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 14B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 33.2% | 60.4% | Qwen3 14B |
| Humanity's Last Exam (Artificial Analysis run) | 4.0% | 4.5% | Qwen3 14B |
| GPQA Diamond (Epoch AI run) | Not measured | 63.8% | Only Qwen3 14B measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Mixtral 8x22B Instruct | Qwen3 14B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 58.0 | Only Qwen3 14B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 58.0% | Only Qwen3 14B measured |
| OTIS Mock AIME | Not measured | 66.4% | Only Qwen3 14B measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mixtral 8x22B Instruct | Qwen3 14B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 53.7% | 77.4% | Qwen3 14B |
| 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 14B | 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 14B | Leader |
|---|---|---|---|
| IFBench | Not measured | 40.5% | Only Qwen3 14B measured |
| Long context reasoning (AA-LCR) | Not measured | 0.0% | Only Qwen3 14B measured |
| DTBench | 55.1% | 64.0% | Qwen3 14B |
| LMCA | Not measured | 18.2% | Only Qwen3 14B measured |
| Fiction.LiveBench | Not measured | 62.5% | Only Qwen3 14B 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 | Mixtral 8x22B Instruct | Qwen3 14B | 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 14B costs less
Repository review
50K input + 3K output tokens
Qwen3 14B costs less
Agent loop
200K input + 10K output tokens
Qwen3 14B 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 14B |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | Open weights | Open weights |
| Released | April 17, 2024 | April 28, 2025 |
| Context window | 66K | 41K |
| Input $/Mtok | $2.00 | $0.11 |
| Output $/Mtok | $6.00 | $0.23 |
| 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 14B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mixtral 8x22B Instruct for a larger context window (66K against 41K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or AA Intelligence Index, where it leads.
Which is cheaper, Mixtral 8x22B Instruct or Qwen3 14B?
Mixtral 8x22B Instruct costs $2.00 per million input tokens and $6.00 per million output tokens; Qwen3 14B costs $0.11 and $0.23. Cost per solved task is known for neither of them.
Which has the bigger context window, Mixtral 8x22B Instruct or Qwen3 14B?
Mixtral 8x22B Instruct, with 66K tokens against 41K.
How many benchmarks is the Mixtral 8x22B Instruct vs Qwen3 14B comparison based on?
7 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 14B's on 0. Where only one model has a result, the page says so instead of naming a winner.