GPT-6.1 Sol Pro vs Qwen3 Coder Next
Updated October 21, 2026. 0 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
- Coding
- No data
- Overall
- No data
- Output $/M
- $10.00
- Coding
- No data
- Overall
- 9.2
- Output $/M
- $0.80
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
GPT-6.1 Sol Pro has the edge where the context window is larger, 1.05M against 262K tokens.
- Shared results
- 0
- GPT-6.1 Sol Pro only
- 0
- Qwen3 Coder Next only
- 11
- Like-for-like categories
- 0 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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 Coder Next has a AA Intelligence Index result, 9.2.
Directional only
Agentic work
AA Agentic Index
No call
Only Qwen3 Coder Next has a AA Agentic Index result, 0.9.
Directional only
Long documents
Listed context window
GPT-6.1 Sol Pro
GPT-6.1 Sol Pro takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 Coder Next
GPT-6.1 Sol Pro $0.0070, Qwen3 Coder Next $0.0005 per turn.
List prices
At a glance
- Coding index
- GPT-6.1 Sol Pro not benchmarked, Qwen3 Coder Next not benchmarked.
- Widest category
- No category has a result for both models.
- Widest single gap
- No benchmark has a result for both models.
- Output price
- GPT-6.1 Sol Pro $10.00 against Qwen3 Coder Next $0.80 per million output tokens, 12.5x apart.
- Context window
- GPT-6.1 Sol Pro takes 1.05M tokens, Qwen3 Coder Next 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.
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | GPT-6.1 Sol Pro | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 36.2 | Only Qwen3 Coder Next measured |
| SciCode | Not measured | 36.2% | Only Qwen3 Coder Next measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 18.2% | Only Qwen3 Coder Next measured |
| WeirdML | Not measured | 34.4% | Only Qwen3 Coder Next measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-6.1 Sol Pro | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 9.2 | Only Qwen3 Coder Next measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-6.1 Sol Pro | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.9 | Only Qwen3 Coder Next measured |
| τ²-Bench Telecom | Not measured | 79.5% | Only Qwen3 Coder Next measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-6.1 Sol Pro | Qwen3 Coder Next | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 73.7% | Only Qwen3 Coder Next measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 10.1% | Only Qwen3 Coder Next measured |
| Neither model has a result on the other 19 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6.1 Sol Pro | Qwen3 Coder Next | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6.1 Sol Pro | Qwen3 Coder Next | Leader |
|---|---|---|---|
| Neither model has a result on 17 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6.1 Sol Pro | Qwen3 Coder Next | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-6.1 Sol Pro | Qwen3 Coder Next | Leader |
|---|---|---|---|
| IFBench | Not measured | 35.2% | Only Qwen3 Coder Next measured |
| Long context reasoning (AA-LCR) | Not measured | 47.0% | Only Qwen3 Coder Next 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-6.1 Sol Pro | Qwen3 Coder Next | 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 Coder Next costs less
Repository review
50K input + 3K output tokens
Qwen3 Coder Next costs less
Agent loop
200K input + 10K output tokens
Qwen3 Coder Next 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 Pro | Qwen3 Coder Next |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | September 29, 2026 | February 4, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $2.00 | $0.12 |
| Output $/Mtok | $10.00 | $0.80 |
| 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-6.1 Sol Pro better than Qwen3 Coder Next for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GPT-6.1 Sol Pro has the edge where the context window is larger, 1.05M against 262K tokens.
Which is cheaper, GPT-6.1 Sol Pro or Qwen3 Coder Next?
GPT-6.1 Sol Pro costs $2.00 per million input tokens and $10.00 per million output tokens; Qwen3 Coder Next costs $0.12 and $0.80. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-6.1 Sol Pro or Qwen3 Coder Next?
GPT-6.1 Sol Pro, with 1.05M tokens against 262K.
How many benchmarks is the GPT-6.1 Sol Pro vs Qwen3 Coder Next comparison based on?
0 benchmarks have a result for both models as of October 21, 2026. GPT-6.1 Sol Pro's coding index rests on 0 independent benchmarks and Qwen3 Coder Next's on 0. Where only one model has a result, the page says so instead of naming a winner.