Mistral Small 4 vs Qwen3 14B
Mistral Small 4 leads Qwen3 14B on the Artificial Analysis Intelligence Index, 11.3 to 6.7, and Qwen3 14B costs 2.6x less per output token ($0.23 vs $0.60 per million). Updated October 22, 2026; 11 benchmarks have a result for both.
Mistral Small 4 or Qwen3 14B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mistral Small 4 for a larger context window (262K against 41K tokens) and work measured by Kagi LLM Benchmark, where it leads.
- Shared results
- 11
- Mistral Small 4 only
- 4
- Qwen3 14B only
- 9
- 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
Mistral Small 4
Mistral Small 4 leads, 11.3 against 6.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Small 4 has a AA Agentic Index result, 0.8.
Directional only
Long documents
Listed context window
Mistral Small 4
Mistral Small 4 takes 262K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 14B
Mistral Small 4 $0.0004, Qwen3 14B $0.0002 per turn.
List prices
At a glance
- Coding index
- Mistral Small 4 not benchmarked, Qwen3 14B not benchmarked.
- Widest category
- General, 16.7 points apart on average over 4 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): Mistral Small 4 49.7% against Qwen3 14B 0.0%, 49.7 points apart on a 0 to 100 scale.
- Output price
- Mistral Small 4 $0.60 against Qwen3 14B $0.23 per million output tokens, 2.6x apart.
- Context window
- Mistral Small 4 takes 262K 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 | Mistral Small 4 | Bars | Qwen3 14B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 1 | ||
| General | 4 | 0 | ||
| All shared boards | 10 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | Mistral Small 4 | Qwen3 14B | Leader |
|---|---|---|---|
| AA Coding Index | 26.6 | Not measured | Only Mistral Small 4 measured |
| Vibe Code Bench (Vals AI) | 0.0% | Not measured | Only Mistral Small 4 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 52.3% | Only Qwen3 14B measured |
| SciCode | 38.8% | 30.7% | Mistral Small 4 |
| Terminal-Bench Hard (Artificial Analysis run) | 17.4% | 5.3% | Mistral Small 4 |
| ALE-Bench | 497.6 | Not measured | Only Mistral Small 4 measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mistral Small 4 | Qwen3 14B | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.3 | 6.7 | Mistral Small 4 |
| Epoch Capabilities Index | Not measured | 138.2 | Only Qwen3 14B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mistral Small 4 | Qwen3 14B | Leader |
|---|---|---|---|
| AA Agentic Index | 0.8 | Not measured | Only Mistral Small 4 measured |
| τ²-Bench Telecom | 41.2% | 34.5% | Mistral Small 4 |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 41.0% | 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 index21
| Benchmark | Mistral Small 4 | Qwen3 14B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 76.9% | 60.4% | Mistral Small 4 |
| Humanity's Last Exam (Artificial Analysis run) | 9.9% | 4.5% | Mistral Small 4 |
| GPQA Diamond (Epoch AI run) | Not measured | 63.8% | Only Qwen3 14B measured |
| Kagi LLM Benchmark | 40.5% | 49.1% | Qwen3 14B |
| 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 | Mistral Small 4 | 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 | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mistral Small 4 | Qwen3 14B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 77.4% | Only Qwen3 14B measured |
| Neither model has a result on the other 17 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Small 4 | Qwen3 14B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Mistral Small 4 | Qwen3 14B | Leader |
|---|---|---|---|
| IFBench | 48.2% | 40.5% | Mistral Small 4 |
| Long context reasoning (AA-LCR) | 49.7% | 0.0% | Mistral Small 4 |
| DTBench | 70.9% | 64.0% | Mistral Small 4 |
| LMCA | 20.6% | 18.2% | Mistral Small 4 |
| 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 | Mistral Small 4 | 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 Small 4 | Qwen3 14B |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | Open weights | Open weights |
| Released | March 16, 2026 | April 28, 2025 |
| Context window | 262K | 41K |
| Input $/Mtok | $0.15 | $0.11 |
| Output $/Mtok | $0.60 | $0.23 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Small 4 better than Qwen3 14B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mistral Small 4 for a larger context window (262K against 41K tokens) and work measured by Kagi LLM Benchmark, where it leads.
Which is cheaper, Mistral Small 4 or Qwen3 14B?
Mistral Small 4 costs $0.15 per million input tokens and $0.60 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, Mistral Small 4 or Qwen3 14B?
Mistral Small 4, with 262K tokens against 41K.
How many benchmarks is the Mistral Small 4 vs Qwen3 14B comparison based on?
11 benchmarks have a result for both models as of October 22, 2026. Mistral Small 4's coding index rests on 1 independent benchmark and Qwen3 14B's on 0. Where only one model has a result, the page says so instead of naming a winner.