GPT-5.6 Sol Pro vs Qwen3.5-Flash
Qwen3.5-Flash costs 76.9x less per output token than GPT-5.6 Sol Pro ($0.26 vs $20.00 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
GPT-5.6 Sol Pro or Qwen3.5-Flash: which to pick
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; GPT-5.6 Sol Pro has none, so the two cannot be ranked on coding.
Pick GPT-5.6 Sol Pro for a larger context window (1.05M against 1M tokens) and work measured by Chess puzzles or DTBench, where it leads.
- Shared results
- 3
- GPT-5.6 Sol Pro only
- 4
- Qwen3.5-Flash only
- 52
- 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
Only Qwen3.5-Flash has a coding score, so no call is made.
One side not measured
Reasoning
GPQA Diamond
No call
Only Qwen3.5-Flash has a GPQA Diamond result, 82.8%.
Directional only
Math
AIME
No call
Only Qwen3.5-Flash has a AIME result, 92.5%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.5-Flash has a MMLU Pro result, 84.1%.
Directional only
Long documents
Listed context window
GPT-5.6 Sol Pro
GPT-5.6 Sol Pro takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
GPT-5.6 Sol Pro $0.014, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.5-Flash has the number, $0.40.
Directional only
At a glance
- Coding index
- GPT-5.6 Sol Pro not benchmarked, Qwen3.5-Flash 41.1 from 2 benchmarks.
- Widest category
- Reasoning, 54.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Chess puzzles: GPT-5.6 Sol Pro 68.0% against Qwen3.5-Flash 14.0%, 54.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol Pro $20.00 against Qwen3.5-Flash $0.26 per million output tokens, 76.9x apart.
- Context window
- GPT-5.6 Sol Pro takes 1.05M tokens, Qwen3.5-Flash 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 | GPT-5.6 Sol Pro | Bars | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Reasoning | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 3 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index9 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Qwen3.5-Flash | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1244 | Only Qwen3.5-Flash measured |
| LiveCodeBench (Vals AI) | Not measured | 83.3% | Only Qwen3.5-Flash measured |
| SWE-bench Verified (Vals AI) | Not measured | 64.4% | Only Qwen3.5-Flash measured |
| WeirdML | 89.4% | Not measured | Only GPT-5.6 Sol Pro measured |
| ALE-Bench | Not measured | 265.9 | Only Qwen3.5-Flash measured |
| LMArena Text: coding | Not measured | 1412 | Only Qwen3.5-Flash measured |
| LMArena Text: software and IT | Not measured | 1416 | Only Qwen3.5-Flash measured |
| LMArena WebDev: React | Not measured | 1229 | Only Qwen3.5-Flash measured |
| LMArena WebDev: HTML | Not measured | 1291 | Only Qwen3.5-Flash 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-5.6 Sol Pro | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Epoch Capabilities Index | Not measured | 143.5 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.6 Sol Pro | Qwen3.5-Flash | Leader |
|---|---|---|---|
| APEX-Agents | 51.4% | Not measured | Only GPT-5.6 Sol Pro measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.6 Sol Pro | Qwen3.5-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 82.8% | Only Qwen3.5-Flash measured |
| MMMU Pro (Vals AI) | Not measured | 81.9% | Only Qwen3.5-Flash measured |
| GPQA Diamond (Epoch AI run) | Not measured | 82.3% | Only Qwen3.5-Flash measured |
| SimpleBench | 71.7% | Not measured | Only GPT-5.6 Sol Pro measured |
| Chess puzzles | 68.0% | 14.0% | GPT-5.6 Sol Pro |
| Mystery game puzzles | Not measured | 20.0% | Only Qwen3.5-Flash measured |
| LMArena Text: hard prompts | Not measured | 1403 | Only Qwen3.5-Flash measured |
| LMArena Text: expert | Not measured | 1407 | Only Qwen3.5-Flash measured |
| LMArena Text: hard prompts in English | Not measured | 1409 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | Not measured | 92.5% | Only Qwen3.5-Flash measured |
| OTIS Mock AIME | Not measured | 84.4% | Only Qwen3.5-Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 18.2% | Only Qwen3.5-Flash measured |
| FrontierMath, tier 4 | 80.5% | Not measured | Only GPT-5.6 Sol Pro measured |
| LMArena Text: math | Not measured | 1407 | Only Qwen3.5-Flash measured |
| LMArena Text: mathematical work | Not measured | 1413 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Qwen3.5-Flash | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.1% | Only Qwen3.5-Flash measured |
| LegalBench (Vals AI) | Not measured | 84.3% | Only Qwen3.5-Flash measured |
| CaseLaw (Vals AI) | Not measured | 56.0% | Only Qwen3.5-Flash measured |
| CorpFin (Vals AI) | Not measured | 63.6% | Only Qwen3.5-Flash measured |
| TaxEval (Vals AI) | Not measured | 72.2% | Only Qwen3.5-Flash measured |
| MortgageTax (Vals AI) | Not measured | 67.4% | Only Qwen3.5-Flash measured |
| MedCode (Vals AI) | Not measured | 33.0% | Only Qwen3.5-Flash measured |
| SimpleQA Verified | Not measured | 20.3% | Only Qwen3.5-Flash measured |
| LMArena Text: science | Not measured | 1415 | Only Qwen3.5-Flash measured |
| LMArena Text: medicine and healthcare | Not measured | 1407 | Only Qwen3.5-Flash measured |
| LMArena Text: legal and government | Not measured | 1398 | Only Qwen3.5-Flash measured |
| LMArena Text: business and finance | Not measured | 1394 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 8 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Sol Pro | Qwen3.5-Flash | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 42.5% | Only Qwen3.5-Flash measured |
| MedScribe (Vals AI) | Not measured | 70.6% | Only Qwen3.5-Flash measured |
| DTBench | 96.0% | 82.9% | GPT-5.6 Sol Pro |
| LMCA | 59.2% | 29.1% | GPT-5.6 Sol Pro |
| LMArena Text | Not measured | 1397 | Only Qwen3.5-Flash measured |
| LMArena Text: instruction following | Not measured | 1374 | Only Qwen3.5-Flash measured |
| LMArena Text: creative writing | Not measured | 1343 | Only Qwen3.5-Flash measured |
| LMArena Text: English | Not measured | 1404 | Only Qwen3.5-Flash measured |
| LMArena Text: non-English | Not measured | 1385 | Only Qwen3.5-Flash measured |
| LMArena Text: Chinese | Not measured | 1445 | Only Qwen3.5-Flash measured |
| LMArena Text: German | Not measured | 1390 | Only Qwen3.5-Flash measured |
| LMArena Text: Japanese | Not measured | 1369 | Only Qwen3.5-Flash measured |
| LMArena Text: Korean | Not measured | 1344 | Only Qwen3.5-Flash measured |
| LMArena Text: French | Not measured | 1413 | Only Qwen3.5-Flash measured |
| LMArena Text: Spanish | Not measured | 1400 | Only Qwen3.5-Flash measured |
| LMArena Text: Russian | Not measured | 1380 | Only Qwen3.5-Flash measured |
| LMArena Text: Polish | Not measured | 1376 | Only Qwen3.5-Flash measured |
| LMArena Text: multi-turn | Not measured | 1393 | Only Qwen3.5-Flash measured |
| LMArena Text: longer queries | Not measured | 1392 | Only Qwen3.5-Flash measured |
| LMArena Text: writing and language | Not measured | 1369 | Only Qwen3.5-Flash measured |
| LMArena Text: entertainment and media | Not measured | 1347 | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Qwen3.5-Flash | 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
Qwen3.5-Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.5-Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-Flash 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 | Qwen3.5-Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | July 9, 2026 | February 25, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $4.00 | $0.065 |
| Output $/Mtok | $20.00 | $0.26 |
| Coding index | Not benchmarked | 41.1 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Sol Pro better than Qwen3.5-Flash for coding?
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; GPT-5.6 Sol Pro has none, so the two cannot be ranked on coding. Pick GPT-5.6 Sol Pro for a larger context window (1.05M against 1M tokens) and work measured by Chess puzzles or DTBench, where it leads.
Which is cheaper, GPT-5.6 Sol Pro or Qwen3.5-Flash?
GPT-5.6 Sol Pro costs $4.00 per million input tokens and $20.00 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Cost per solved task is known for Qwen3.5-Flash only.
Which has the bigger context window, GPT-5.6 Sol Pro or Qwen3.5-Flash?
GPT-5.6 Sol Pro, with 1.05M tokens against 1M.
How many benchmarks is the GPT-5.6 Sol Pro vs Qwen3.5-Flash 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 Qwen3.5-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.