GPT-5.6 Sol Pro vs Qwen3 235B A22B Instruct 2507
Qwen3 235B A22B Instruct 2507 costs 25.8x less per output token than GPT-5.6 Sol Pro ($0.775 vs $20.00 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
GPT-5.6 Sol Pro or Qwen3 235B A22B Instruct 2507: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.6 Sol Pro for a larger context window (1.05M against 262K tokens).
- Shared results
- 3
- GPT-5.6 Sol Pro only
- 4
- Qwen3 235B A22B Instruct 2507 only
- 21
- Like-for-like categories
- 2 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
No call
Only Qwen3 235B A22B Instruct 2507 has a AA Intelligence Index result, 12.0.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3 235B A22B Instruct 2507 has a Design Arena: websites result, 1058.
Directional only
Long documents
Listed context window
GPT-5.6 Sol Pro
GPT-5.6 Sol Pro takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 235B A22B Instruct 2507
GPT-5.6 Sol Pro $0.014, Qwen3 235B A22B Instruct 2507 $0.0005 per turn.
List prices
At a glance
- Coding index
- GPT-5.6 Sol Pro not benchmarked, Qwen3 235B A22B Instruct 2507 not benchmarked.
- Widest category
- Coding, 50.7 points apart on average over 1 shared benchmark.
- Widest single gap
- WeirdML: GPT-5.6 Sol Pro 89.4% against Qwen3 235B A22B Instruct 2507 38.7%, 50.7 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol Pro $20.00 against Qwen3 235B A22B Instruct 2507 $0.775 per million output tokens, 25.8x apart.
- Context window
- GPT-5.6 Sol Pro takes 1.05M tokens, Qwen3 235B A22B 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-5.6 Sol Pro | Bars | Qwen3 235B A22B Instruct 2507 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 3 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-5.6 Sol Pro | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 78.8% | Only Qwen3 235B A22B Instruct 2507 measured |
| SciCode | Not measured | 41.4% | Only Qwen3 235B A22B Instruct 2507 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 15.2% | Only Qwen3 235B A22B Instruct 2507 measured |
| WeirdML | 89.4% | 38.7% | GPT-5.6 Sol Pro |
| Neither model has a result on the other 39 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.6 Sol Pro | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 12.0 | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 53.2% | Only Qwen3 235B A22B Instruct 2507 measured |
| APEX-Agents | 51.4% | Not measured | Only GPT-5.6 Sol Pro measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 52.1% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 79.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 15.9% | Only Qwen3 235B A22B Instruct 2507 measured |
| ARC-AGI | Not measured | 11.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| ARC-AGI-2 | Not measured | 1.3% | Only Qwen3 235B A22B Instruct 2507 measured |
| SimpleBench | 71.7% | Not measured | Only GPT-5.6 Sol Pro measured |
| Chess puzzles | 68.0% | Not measured | Only GPT-5.6 Sol Pro measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 91.0 | Only Qwen3 235B A22B Instruct 2507 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 91.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| FrontierMath, tier 4 | 80.5% | Not measured | Only GPT-5.6 Sol Pro measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.6 Sol Pro | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 84.3% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 19 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1058 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: UI components | Not measured | 963 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: game dev | Not measured | 951 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: data viz | Not measured | 1070 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: 3D | Not measured | 994 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: all code categories | Not measured | 1038 | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Sol Pro | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| IFBench | Not measured | 51.2% | Only Qwen3 235B A22B Instruct 2507 measured |
| Long context reasoning (AA-LCR) | Not measured | 72.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| DTBench | 96.0% | 78.4% | GPT-5.6 Sol Pro |
| LMCA | 59.2% | 23.5% | GPT-5.6 Sol Pro |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Qwen3 235B A22B 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
Qwen3 235B A22B Instruct 2507 costs less
Repository review
50K input + 3K output tokens
Qwen3 235B A22B Instruct 2507 costs less
Agent loop
200K input + 10K output tokens
Qwen3 235B A22B 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-5.6 Sol Pro | Qwen3 235B A22B Instruct 2507 |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | July 21, 2025 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $4.00 | $0.145 |
| Output $/Mtok | $20.00 | $0.775 |
| 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 GPT-5.6 Sol Pro better than Qwen3 235B A22B Instruct 2507 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.6 Sol Pro for a larger context window (1.05M against 262K tokens).
Which is cheaper, GPT-5.6 Sol Pro or Qwen3 235B A22B Instruct 2507?
GPT-5.6 Sol Pro costs $4.00 per million input tokens and $20.00 per million output tokens; Qwen3 235B A22B Instruct 2507 costs $0.145 and $0.775. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.6 Sol Pro or Qwen3 235B A22B Instruct 2507?
GPT-5.6 Sol Pro, with 1.05M tokens against 262K.
How many benchmarks is the GPT-5.6 Sol Pro vs Qwen3 235B A22B Instruct 2507 comparison based on?
3 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Sol Pro's coding index rests on 0 independent benchmarks and Qwen3 235B A22B Instruct 2507's on 0. Where only one model has a result, the page says so instead of naming a winner.