GPT-6.1 Sol vs Qwen3 30B A3B Instruct 2507
GPT-6.1 Sol leads Qwen3 30B A3B Instruct 2507 on the Artificial Analysis Intelligence Index, 51.8 to 7.5, and Qwen3 30B A3B Instruct 2507 costs 33.3x less per output token ($0.30 vs $10.00 per million). Updated October 22, 2026; 32 benchmarks have a result for both.
vs boards won of 32 shared
GPT-6.1 Sol or Qwen3 30B A3B Instruct 2507: which to pick
Only GPT-6.1 Sol has a coding score, 82.4 on the Vibe Coding Index; Qwen3 30B A3B Instruct 2507 has none, so the two cannot be ranked on coding.
Pick Qwen3 30B A3B Instruct 2507 for output tokens 33.3x cheaper ($0.30 vs $10.00 per million).
Model AGPT-6.1 SolOpenAI82.4/1005 independent benchmarksNo cost per solved taskModel BQwen3 30B A3B Instruct 2507QwenNot benchmarkedNo coding benchmarkNo cost per solved task
- Shared results
- 32
- GPT-6.1 Sol only
- 71
- Qwen3 30B A3B Instruct 2507 only
- 13
- 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 GPT-6.1 Sol has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-6.1 Sol
GPT-6.1 Sol leads, 51.8 against 7.5.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-6.1 Sol has a Design Arena: websites result, 1321.
Directional only
General
Vals Index
No call
Only GPT-6.1 Sol has a Vals Index result, 63.9%.
Directional only
Long documents
Listed context window
GPT-6.1 Sol
GPT-6.1 Sol takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 30B A3B Instruct 2507
GPT-6.1 Sol $0.0070, Qwen3 30B A3B Instruct 2507 $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-6.1 Sol 82.4 from 5 benchmarks, Qwen3 30B A3B Instruct 2507 not benchmarked.
- Widest category
- Intelligence, 44.3 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: GPT-6.1 Sol 51.8 against Qwen3 30B A3B Instruct 2507 7.5, 44.3 points apart on a 0 to 100 scale.
- Output price
- GPT-6.1 Sol $10.00 against Qwen3 30B A3B Instruct 2507 $0.30 per million output tokens, 33.3x apart.
- Context window
- GPT-6.1 Sol takes 1.05M tokens, Qwen3 30B A3B Instruct 2507 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 | GPT-6.1 Sol | Bars | Qwen3 30B A3B Instruct 2507 | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 16 | 0 | ||
| All shared boards | 32 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | GPT-6.1 Sol | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | Not measured | Only GPT-6.1 Sol measured |
| LMArena WebDev | 1753 | Not measured | Only GPT-6.1 Sol measured |
| Terminal-Bench 4 (Vals AI) | 55.0% | Not measured | Only GPT-6.1 Sol measured |
| Vibe Code Bench (Vals AI) | 88.9% | Not measured | Only GPT-6.1 Sol measured |
| Code migration (Vals AI) | 65.1% | Not measured | Only GPT-6.1 Sol measured |
| ProgramBench (Vals AI) | 3.0% | Not measured | Only GPT-6.1 Sol measured |
| IOI (Vals AI) | 96.9% | Not measured | Only GPT-6.1 Sol measured |
| SWE-bench Multimodal (Vals AI) | 43.5% | Not measured | Only GPT-6.1 Sol measured |
| CyberBench (Vals AI) | 39.3% | Not measured | Only GPT-6.1 Sol measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 70.7% | Only Qwen3 30B A3B Instruct 2507 measured |
| SciCode | 55.8% | 33.0% | GPT-6.1 Sol |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 6.1% | Only Qwen3 30B A3B Instruct 2507 measured |
| FrontierCode | 50.2% | Not measured | Only GPT-6.1 Sol measured |
| ALE-Bench | 2743.8 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: coding | 1478 | 1416 | GPT-6.1 Sol |
| LMArena Text: software and IT | 1476 | 1417 | GPT-6.1 Sol |
| LMArena WebDev: React | 1754 | Not measured | Only GPT-6.1 Sol measured |
| LMArena WebDev: HTML | 1800 | Not measured | Only GPT-6.1 Sol measured |
| LMArena WebDev: from an image | 1702 | Not measured | Only GPT-6.1 Sol measured |
| SciCode | 9.2% | Not measured | Only GPT-6.1 Sol measured |
| SWE-Bench Pro V2 | 96.1% | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6.1 Sol | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | 51.8 | 7.5 | GPT-6.1 Sol |
| Epoch Capabilities Index | 168.4 | Not measured | Only GPT-6.1 Sol measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index20 boards, none shared
| Benchmark | GPT-6.1 Sol | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1307 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: agents building mobile apps | 1224 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: spreadsheets | 1270 | Not measured | Only GPT-6.1 Sol measured |
| LiveBench research | 83.5% | Not measured | Only GPT-6.1 Sol measured |
| LiveBench browser | 88.0% | Not measured | Only GPT-6.1 Sol measured |
| LiveBench long agentic | 73.0% | Not measured | Only GPT-6.1 Sol measured |
| SREBench (Vals AI) | 50.8% | Not measured | Only GPT-6.1 Sol measured |
| Finance Agent (Vals AI) | 52.0% | Not measured | Only GPT-6.1 Sol measured |
| Tax Agent Bench (Vals AI) | 21.8% | Not measured | Only GPT-6.1 Sol measured |
| Legal Research Bench (Vals AI) | 38.5% | Not measured | Only GPT-6.1 Sol measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 6.3% | Not measured | Only GPT-6.1 Sol measured |
| Excel Modeling Benchmark (Vals AI) | 70.8% | Not measured | Only GPT-6.1 Sol measured |
| BioMysteryBench (Vals AI) | 79.6% | Not measured | Only GPT-6.1 Sol measured |
| Terminal-Bench-Science (Vals AI) | 52.9% | Not measured | Only GPT-6.1 Sol measured |
| Vals Science Index | 59.6% | Not measured | Only GPT-6.1 Sol measured |
| Robo-bench (Vals AI) | 28.3% | Not measured | Only GPT-6.1 Sol measured |
| τ²-Bench Telecom | Not measured | 28.1% | Only Qwen3 30B A3B Instruct 2507 measured |
| APEX-Agents | 60.0% | Not measured | Only GPT-6.1 Sol measured |
| SpireClimb | 24.7% | Not measured | Only GPT-6.1 Sol measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 41.4% | Only Qwen3 30B A3B Instruct 2507 measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-6.1 Sol | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| LiveBench reasoning | 92.6% | Not measured | Only GPT-6.1 Sol measured |
| MysteryMechanism (Vals AI) | 46.4% | Not measured | Only GPT-6.1 Sol measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 70.7% | Only Qwen3 30B A3B Instruct 2507 measured |
| Humanity's Last Exam (Artificial Analysis run) | 52.9% | 10.3% | GPT-6.1 Sol |
| GPQA Diamond (Epoch AI run) | 95.4% | 55.6% | GPT-6.1 Sol |
| ARC-AGI | 98.5% | Not measured | Only GPT-6.1 Sol measured |
| ARC-AGI-2 | 94.2% | Not measured | Only GPT-6.1 Sol measured |
| Chess puzzles | 68.0% | Not measured | Only GPT-6.1 Sol measured |
| Mystery game puzzles | 80.0% | Not measured | Only GPT-6.1 Sol measured |
| EBR-bench | 54.3% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: hard prompts | 1463 | 1398 | GPT-6.1 Sol |
| LMArena Text: expert | 1489 | 1397 | GPT-6.1 Sol |
| LMArena Text: hard prompts in English | 1465 | 1406 | GPT-6.1 Sol |
| LMArena Vision: diagrams | 1306 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision: homework | 1356 | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-6.1 Sol | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 66.3 | Only Qwen3 30B A3B Instruct 2507 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 66.3% | Only Qwen3 30B A3B Instruct 2507 measured |
| OTIS Mock AIME | 100.0% | 62.2% | GPT-6.1 Sol |
| FrontierMath, tiers 1 to 3 | 93.7% | Not measured | Only GPT-6.1 Sol measured |
| FrontierMath, tier 4 | 100.0% | Not measured | Only GPT-6.1 Sol measured |
| ProofBench | 99.0% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: math | 1489 | 1394 | GPT-6.1 Sol |
| LMArena Text: mathematical work | 1513 | 1411 | GPT-6.1 Sol |
| MathArena | 84.0% | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-6.1 Sol | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| MedCode (Vals AI) | 48.8% | Not measured | Only GPT-6.1 Sol measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 80.5% | Only Qwen3 30B A3B Instruct 2507 measured |
| SimpleQA Verified | 73.9% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: science | 1444 | 1393 | GPT-6.1 Sol |
| LMArena Text: medicine and healthcare | 1433 | 1392 | GPT-6.1 Sol |
| LMArena Text: legal and government | 1471 | 1387 | GPT-6.1 Sol |
| LMArena Text: business and finance | 1445 | 1389 | GPT-6.1 Sol |
| FACTS Benchmark Suite | 75.3% | Not measured | Only GPT-6.1 Sol measured |
| PRBench Finance | 46.2% | Not measured | Only GPT-6.1 Sol measured |
| PRBench Legal | 47.9% | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-6.1 Sol | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| Design Arena: websites | 1321 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: UI components | 1323 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: game dev | 1393 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: data viz | 1286 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: 3D | 1458 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: all code categories | 1346 | Not measured | Only GPT-6.1 Sol measured |
| LiveBench design | 82.1% | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index160
| Benchmark | GPT-6.1 Sol | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| Vals Index | 63.9% | Not measured | Only GPT-6.1 Sol measured |
| LiveBench data analysis | 82.7% | Not measured | Only GPT-6.1 Sol measured |
| Public Benefits Bench (Vals AI) | 59.3% | Not measured | Only GPT-6.1 Sol measured |
| SAGE (Vals AI) | 46.5% | Not measured | Only GPT-6.1 Sol measured |
| MedScribe (Vals AI) | 86.5% | Not measured | Only GPT-6.1 Sol measured |
| IFBench | Not measured | 50.7% | Only Qwen3 30B A3B Instruct 2507 measured |
| Long context reasoning (AA-LCR) | 84.0% | 61.3% | GPT-6.1 Sol |
| DTBench | 96.5% | 67.2% | GPT-6.1 Sol |
| LMCA | 62.8% | 22.4% | GPT-6.1 Sol |
| Furniture assembly | 80.0% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text | 1443 | 1384 | GPT-6.1 Sol |
| LMArena Text: instruction following | 1466 | 1363 | GPT-6.1 Sol |
| LMArena Text: creative writing | 1425 | 1317 | GPT-6.1 Sol |
| LMArena Vision | 1288 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: English | 1450 | 1391 | GPT-6.1 Sol |
| LMArena Text: non-English | 1433 | 1372 | GPT-6.1 Sol |
| LMArena Text: Chinese | 1487 | 1432 | GPT-6.1 Sol |
| LMArena Text: German | Not measured | 1379 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: Japanese | Not measured | 1337 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: Korean | Not measured | 1330 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: French | 1460 | 1419 | GPT-6.1 Sol |
| LMArena Text: Spanish | 1439 | 1404 | GPT-6.1 Sol |
| LMArena Text: Russian | 1457 | 1369 | GPT-6.1 Sol |
| LMArena Text: Polish | Not measured | 1353 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: multi-turn | 1442 | 1378 | GPT-6.1 Sol |
| LMArena Text: longer queries | 1458 | 1378 | GPT-6.1 Sol |
| LMArena Text: writing and language | 1444 | 1336 | GPT-6.1 Sol |
| LMArena Text: entertainment and media | 1426 | 1334 | GPT-6.1 Sol |
| LMArena Vision: English | 1274 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision: reading text | 1289 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision: creative writing | 1301 | Not measured | Only GPT-6.1 Sol measured |
| MultiChallenge | 82.0% | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6.1 Sol | Qwen3 30B A3B Instruct 2507 | 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
GPT-6.1 Sol$0.0070Fits in one request
Qwen3 30B A3B Instruct 2507$0.0002Fits in one request
Qwen3 30B A3B Instruct 2507 costs less
Repository review
50K input + 3K output tokens
GPT-6.1 Sol$0.130Fits in one request
Qwen3 30B A3B Instruct 2507$0.0054Fits in one request
Qwen3 30B A3B Instruct 2507 costs less
Agent loop
200K input + 10K output tokens
GPT-6.1 Sol$0.500Fits in one request
Qwen3 30B A3B Instruct 2507$0.021Fits in one request
Qwen3 30B A3B Instruct 2507 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 | OpenAI GPT-6.1 Sol | Qwen3 30B A3B Instruct 2507 |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | September 29, 2026 | July 29, 2025 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $2.00 | $0.09 |
| Output $/Mtok | $10.00 | $0.30 |
| Coding index | 82.4 | Not benchmarked |
| Independent measurements behind it | 5 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-6.1 Sol better than Qwen3 30B A3B Instruct 2507 for coding?
Only GPT-6.1 Sol has a coding score, 82.4 on the Vibe Coding Index; Qwen3 30B A3B Instruct 2507 has none, so the two cannot be ranked on coding. Pick Qwen3 30B A3B Instruct 2507 for output tokens 33.3x cheaper ($0.30 vs $10.00 per million).
Which is cheaper, GPT-6.1 Sol or Qwen3 30B A3B Instruct 2507?
GPT-6.1 Sol costs $2.00 per million input tokens and $10.00 per million output tokens; Qwen3 30B A3B Instruct 2507 costs $0.09 and $0.30. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-6.1 Sol or Qwen3 30B A3B Instruct 2507?
GPT-6.1 Sol, with 1.05M tokens against 262K.
How many benchmarks is the GPT-6.1 Sol vs Qwen3 30B A3B Instruct 2507 comparison based on?
32 benchmarks have a result for both models as of October 22, 2026. GPT-6.1 Sol's coding index rests on 5 independent benchmarks and Qwen3 30B A3B Instruct 2507's on 0. Where only one model has a result, the page says so instead of naming a winner.