Qwen3 14B vs Qwen3.7 Max
Qwen3.7 Max leads Qwen3 14B on the Artificial Analysis Intelligence Index, 29.5 to 6.7, and Qwen3 14B costs 19.2x less per output token ($0.23 vs $4.425 per million). Updated October 22, 2026; 13 benchmarks have a result for both.
Qwen3 14B or Qwen3.7 Max: which to pick
Only Qwen3.7 Max has a coding score, 54.1 on the Vibe Coding Index; Qwen3 14B has none, so the two cannot be ranked on coding.
Pick Qwen3 14B for output tokens 19.2x cheaper ($0.23 vs $4.425 per million).
- Shared results
- 13
- Qwen3 14B only
- 7
- Qwen3.7 Max only
- 46
- Like-for-like categories
- 6 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
Only Qwen3.7 Max has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.7 Max
Qwen3.7 Max leads, 29.5 against 6.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.7 Max has a AA Agentic Index result, 22.5.
Directional only
Reasoning
GPQA Diamond
No call
Only Qwen3.7 Max has a GPQA Diamond result, 90.2%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.7 Max has a MMLU Pro result, 89.3%.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3.7 Max has a Design Arena: websites result, 1274.
Directional only
Long documents
Listed context window
Qwen3.7 Max
Qwen3.7 Max takes 1M tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 14B
Qwen3 14B $0.0002, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.7 Max has the number, $4.69.
Directional only
At a glance
- Coding index
- Qwen3 14B not benchmarked, Qwen3.7 Max 54.1 from 3 benchmarks.
- Widest category
- Agentic work, 60.2 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Qwen3 14B 0.0% against Qwen3.7 Max 79.0%, 79.0 points apart on a 0 to 100 scale.
- Output price
- Qwen3 14B $0.23 against Qwen3.7 Max $4.425 per million output tokens, 19.2x apart.
- Context window
- Qwen3 14B takes 41K tokens, Qwen3.7 Max 1M.
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 | Qwen3 14B | Bars | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 1 | ||
| General | 0 | 4 | ||
| All shared boards | 0 | 13 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Qwen3 14B | Qwen3.7 Max | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1515 | Only Qwen3.7 Max measured |
| AA Coding Index | Not measured | 66.0 | Only Qwen3.7 Max measured |
| LiveCodeBench (Vals AI) | Not measured | 87.1% | Only Qwen3.7 Max measured |
| SWE-bench Verified (Vals AI) | Not measured | 68.8% | Only Qwen3.7 Max measured |
| Vibe Code Bench (Vals AI) | Not measured | 47.7% | Only Qwen3.7 Max measured |
| Code migration (Vals AI) | Not measured | 13.1% | Only Qwen3.7 Max measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 61.0% | Only Qwen3.7 Max measured |
| LiveCodeBench (Artificial Analysis run) | 52.3% | Not measured | Only Qwen3 14B measured |
| SciCode | 30.7% | 49.5% | Qwen3.7 Max |
| Terminal-Bench Hard (Artificial Analysis run) | 5.3% | 50.8% | Qwen3.7 Max |
| ALE-Bench | Not measured | 1189.4 | Only Qwen3.7 Max measured |
| LMArena WebDev: React | Not measured | 1514 | Only Qwen3.7 Max measured |
| LMArena WebDev: HTML | Not measured | 1512 | Only Qwen3.7 Max measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Qwen3 14B | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 29.5 | Qwen3.7 Max |
| Epoch Capabilities Index | 138.2 | 154.0 | Qwen3.7 Max |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 14B | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 22.5 | Only Qwen3.7 Max measured |
| Design Arena: agents building web apps | Not measured | 1208 | Only Qwen3.7 Max measured |
| Design Arena: agents building full-stack apps | Not measured | 1191 | Only Qwen3.7 Max measured |
| Design Arena: agents building mobile apps | Not measured | 1155 | Only Qwen3.7 Max measured |
| Design Arena: agents building Android apps | Not measured | 1159 | Only Qwen3.7 Max measured |
| Design Arena: agents building Godot games | Not measured | 1187 | Only Qwen3.7 Max measured |
| Design Arena: agents building games | Not measured | 1161 | Only Qwen3.7 Max measured |
| Design Arena: HTML slides | Not measured | 1108 | Only Qwen3.7 Max measured |
| Design Arena: PowerPoint slides | Not measured | 1171 | Only Qwen3.7 Max measured |
| Finance Agent (Vals AI) | Not measured | 47.8% | Only Qwen3.7 Max measured |
| Tax Agent Bench (Vals AI) | Not measured | 15.8% | Only Qwen3.7 Max measured |
| Legal Research Bench (Vals AI) | Not measured | 25.5% | Only Qwen3.7 Max measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 57.0% | Only Qwen3.7 Max measured |
| τ²-Bench Telecom | 34.5% | 94.7% | Qwen3.7 Max |
| Berkeley Function Calling Leaderboard (BFCL) | 41.0% | Not measured | Only Qwen3 14B measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Qwen3 14B | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 90.2% | Only Qwen3.7 Max measured |
| GPQA Diamond (Artificial Analysis run) | 60.4% | 92.3% | Qwen3.7 Max |
| Humanity's Last Exam (Artificial Analysis run) | 4.5% | 40.5% | Qwen3.7 Max |
| GPQA Diamond (Epoch AI run) | 63.8% | 90.9% | Qwen3.7 Max |
| SimpleBench | Not measured | 70.4% | Only Qwen3.7 Max measured |
| Chess puzzles | Not measured | 26.0% | Only Qwen3.7 Max measured |
| Mystery game puzzles | Not measured | 32.0% | Only Qwen3.7 Max measured |
| EBR-bench | Not measured | 9.5% | Only Qwen3.7 Max measured |
| Kagi LLM Benchmark | 49.1% | Not measured | Only Qwen3 14B measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 14B | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Math Index | 58.0 | Not measured | Only Qwen3 14B measured |
| AIME 2025 (Artificial Analysis run) | 58.0% | Not measured | Only Qwen3 14B measured |
| OTIS Mock AIME | 66.4% | 95.6% | Qwen3.7 Max |
| FrontierMath, tiers 1 to 3 | Not measured | 64.6% | Only Qwen3.7 Max measured |
| FrontierMath, tier 4 | Not measured | 34.1% | Only Qwen3.7 Max measured |
| ProofBench | Not measured | 26.0% | Only Qwen3.7 Max measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Qwen3 14B | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 89.3% | Only Qwen3.7 Max measured |
| LegalBench (Vals AI) | Not measured | 84.9% | Only Qwen3.7 Max measured |
| CorpFin (Vals AI) | Not measured | 63.7% | Only Qwen3.7 Max measured |
| TaxEval (Vals AI) | Not measured | 75.3% | Only Qwen3.7 Max measured |
| MedCode (Vals AI) | Not measured | 38.8% | Only Qwen3.7 Max measured |
| MMLU Pro (Artificial Analysis run) | 77.4% | Not measured | Only Qwen3 14B measured |
| SimpleQA Verified | Not measured | 55.8% | Only Qwen3.7 Max measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Qwen3 14B | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1274 | Only Qwen3.7 Max measured |
| Design Arena: UI components | Not measured | 1267 | Only Qwen3.7 Max measured |
| Design Arena: game dev | Not measured | 1264 | Only Qwen3.7 Max measured |
| Design Arena: data viz | Not measured | 1275 | Only Qwen3.7 Max measured |
| Design Arena: 3D | Not measured | 1265 | Only Qwen3.7 Max measured |
| Design Arena: all code categories | Not measured | 1275 | Only Qwen3.7 Max measured |
| Design Arena: SVG | Not measured | 1216 | Only Qwen3.7 Max measured |
| Design Arena: ASCII art | Not measured | 1211 | Only Qwen3.7 Max measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Qwen3 14B | Qwen3.7 Max | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 79.4% | Only Qwen3.7 Max measured |
| IFBench | 40.5% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | 0.0% | 79.0% | Qwen3.7 Max |
| DTBench | 64.0% | 92.3% | Qwen3.7 Max |
| LMCA | 18.2% | 44.0% | Qwen3.7 Max |
| Fiction.LiveBench | 62.5% | Not measured | Only Qwen3 14B measured |
| Neither model has a result on the other 40 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3 14B | Qwen3.7 Max | 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 | Qwen3 14B | Qwen3.7 Max |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | No public weights listed |
| Released | April 28, 2025 | May 21, 2026 |
| Context window | 41K | 1M |
| Input $/Mtok | $0.11 | $1.475 |
| Output $/Mtok | $0.23 | $4.425 |
| Coding index | Not benchmarked | 54.1 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3 14B better than Qwen3.7 Max for coding?
Only Qwen3.7 Max has a coding score, 54.1 on the Vibe Coding Index; Qwen3 14B has none, so the two cannot be ranked on coding. Pick Qwen3 14B for output tokens 19.2x cheaper ($0.23 vs $4.425 per million).
Which is cheaper, Qwen3 14B or Qwen3.7 Max?
Qwen3 14B costs $0.11 per million input tokens and $0.23 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Cost per solved task is known for Qwen3.7 Max only.
Which has the bigger context window, Qwen3 14B or Qwen3.7 Max?
Qwen3.7 Max, with 1M tokens against 41K.
How many benchmarks is the Qwen3 14B vs Qwen3.7 Max comparison based on?
13 benchmarks have a result for both models as of October 22, 2026. Qwen3 14B's coding index rests on 0 independent benchmarks and Qwen3.7 Max's on 3. Where only one model has a result, the page says so instead of naming a winner.