GPT-5 Pro vs Qwen3 235B A22B Instruct 2507
Qwen3 235B A22B Instruct 2507 costs 154.8x less per output token than GPT-5 Pro ($0.775 vs $120.00 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
GPT-5 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 Pro for a larger context window (400K against 262K tokens).
- Shared results
- 3
- GPT-5 Pro only
- 8
- 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 Pro
GPT-5 Pro takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 235B A22B Instruct 2507
GPT-5 Pro $0.075, Qwen3 235B A22B Instruct 2507 $0.0005 per turn.
List prices
At a glance
- Coding index
- GPT-5 Pro not benchmarked, Qwen3 235B A22B Instruct 2507 not benchmarked.
- Widest category
- Reasoning, 38.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- ARC-AGI: GPT-5 Pro 70.2% against Qwen3 235B A22B Instruct 2507 11.0%, 59.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5 Pro $120.00 against Qwen3 235B A22B Instruct 2507 $0.775 per million output tokens, 154.8x apart.
- Context window
- GPT-5 Pro takes 400K 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 Pro | Bars | Qwen3 235B A22B Instruct 2507 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| All shared boards | 3 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-5 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 | 60.4% | 38.7% | GPT-5 Pro |
| Neither model has a result on the other 39 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5 Pro | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 12.0 | Only Qwen3 235B A22B Instruct 2507 measured |
| Epoch Capabilities Index | 150.6 | Not measured | Only GPT-5 Pro measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5 Pro | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 53.2% | Only Qwen3 235B A22B Instruct 2507 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 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5 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 | 70.2% | 11.0% | GPT-5 Pro |
| ARC-AGI-2 | 18.3% | 1.3% | GPT-5 Pro |
| SimpleBench | 61.6% | Not measured | Only GPT-5 Pro measured |
| Chess puzzles | 48.0% | Not measured | Only GPT-5 Pro measured |
| Kagi LLM Benchmark | 76.8% | Not measured | Only GPT-5 Pro measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5 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, tiers 1 to 3 | 55.8% | Not measured | Only GPT-5 Pro measured |
| FrontierMath, tier 4 | 19.5% | Not measured | Only GPT-5 Pro measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5 Pro | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 84.3% | Only Qwen3 235B A22B Instruct 2507 measured |
| PRBench Finance | 51.1% | Not measured | Only GPT-5 Pro measured |
| PRBench Legal | 49.9% | Not measured | Only GPT-5 Pro measured |
| Neither model has a result on the other 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5 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 | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5 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 | Not measured | 78.4% | Only Qwen3 235B A22B Instruct 2507 measured |
| LMCA | Not measured | 23.5% | Only Qwen3 235B A22B Instruct 2507 measured |
| 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 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 Pro | Qwen3 235B A22B Instruct 2507 |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | October 6, 2025 | July 21, 2025 |
| Context window | 400K | 262K |
| Input $/Mtok | $15.00 | $0.145 |
| Output $/Mtok | $120.00 | $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 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 Pro for a larger context window (400K against 262K tokens).
Which is cheaper, GPT-5 Pro or Qwen3 235B A22B Instruct 2507?
GPT-5 Pro costs $15.00 per million input tokens and $120.00 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 Pro or Qwen3 235B A22B Instruct 2507?
GPT-5 Pro, with 400K tokens against 262K.
How many benchmarks is the GPT-5 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 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.