GPT-5.1-Codex vs Qwen3.5-Flash
Qwen3.5-Flash leads GPT-5.1-Codex on coding, 41.1 to 15.3 on the Vibe Coding Index, and Qwen3.5-Flash costs 38.5x less per output token ($0.26 vs $10.00 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
GPT-5.1-Codex or Qwen3.5-Flash: which to pick
Pick Qwen3.5-Flash for coding: it scores 41.1 on the Vibe Coding Index against 15.3 for GPT-5.1-Codex.
Pick GPT-5.1-Codex for work measured by LMArena WebDev or LiveCodeBench (Vals AI), where it leads.
- Shared results
- 4
- GPT-5.1-Codex only
- 22
- Qwen3.5-Flash only
- 51
- Like-for-like categories
- 1 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
Qwen3.5-Flash
Qwen3.5-Flash scores 41.1 against 15.3.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
No call
Only GPT-5.1-Codex has a AA Intelligence Index result, 23.7.
Directional only
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
Web design
Design Arena: websites
No call
Only GPT-5.1-Codex has a Design Arena: websites result, 1161.
Directional only
Long documents
Listed context window
Qwen3.5-Flash
Qwen3.5-Flash takes 1M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
GPT-5.1-Codex $0.0063, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Qwen3.5-Flash
GPT-5.1-Codex $0.892, Qwen3.5-Flash $0.40.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.1-Codex 15.3 from 2 benchmarks, Qwen3.5-Flash 41.1 from 2 benchmarks.
- Widest category
- Coding, 14.4 points apart on average over 4 shared benchmarks.
- Widest single gap
- ALE-Bench: GPT-5.1-Codex 1244.9 against Qwen3.5-Flash 265.9, 32.6 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex $10.00 against Qwen3.5-Flash $0.26 per million output tokens, 38.5x apart.
- Context window
- GPT-5.1-Codex takes 400K 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-5.1-Codex | Bars | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| All shared boards | 4 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GPT-5.1-Codex | Qwen3.5-Flash | Leader |
|---|---|---|---|
| SWE-bench Verified | 66.0% | Not measured | Only GPT-5.1-Codex measured |
| LMArena WebDev | 1337 | 1244 | GPT-5.1-Codex |
| LiveCodeBench (Vals AI) | 85.5% | 83.3% | GPT-5.1-Codex |
| SWE-bench Verified (Vals AI) | Not measured | 64.4% | Only Qwen3.5-Flash measured |
| Vibe Code Bench (Vals AI) | 13.1% | Not measured | Only GPT-5.1-Codex measured |
| LiveCodeBench (Artificial Analysis run) | 84.9% | Not measured | Only GPT-5.1-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | 34.8% | Not measured | Only GPT-5.1-Codex measured |
| Terminal-Bench | 57.8% | Not measured | Only GPT-5.1-Codex measured |
| ALE-Bench | 1244.9 | 265.9 | GPT-5.1-Codex |
| 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 | 1346 | 1291 | GPT-5.1-Codex |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.1-Codex | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.7 | Not measured | Only GPT-5.1-Codex measured |
| Epoch Capabilities Index | Not measured | 143.5 | Only Qwen3.5-Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.1-Codex | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1018 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: agents building full-stack apps | 1086 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: agents building mobile apps | 1191 | Not measured | Only GPT-5.1-Codex measured |
| τ²-Bench Telecom | 83.0% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GPT-5.1-Codex | 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 (Artificial Analysis run) | 86.0% | Not measured | Only GPT-5.1-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 25.7% | Not measured | Only GPT-5.1-Codex measured |
| GPQA Diamond (Epoch AI run) | Not measured | 82.3% | Only Qwen3.5-Flash measured |
| Chess puzzles | Not measured | 14.0% | Only Qwen3.5-Flash measured |
| 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 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.1-Codex | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | Not measured | 92.5% | Only Qwen3.5-Flash measured |
| AA Math Index | 95.7 | Not measured | Only GPT-5.1-Codex measured |
| AIME 2025 (Artificial Analysis run) | 95.7% | Not measured | Only GPT-5.1-Codex 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 |
| 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 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | GPT-5.1-Codex | 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 |
| MMLU Pro (Artificial Analysis run) | 86.0% | Not measured | Only GPT-5.1-Codex 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 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.1-Codex | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1161 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: UI components | 1235 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: game dev | 1137 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: data viz | 1213 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: all code categories | 1152 | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 4 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index23 boards, none shared
| Benchmark | GPT-5.1-Codex | 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 |
| IFBench | 70.0% | Not measured | Only GPT-5.1-Codex measured |
| Long context reasoning (AA-LCR) | 69.3% | Not measured | Only GPT-5.1-Codex measured |
| DTBench | Not measured | 82.9% | Only Qwen3.5-Flash measured |
| LMCA | Not measured | 29.1% | Only Qwen3.5-Flash measured |
| 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 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex | 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-5.1-Codex | Qwen3.5-Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | November 13, 2025 | February 25, 2026 |
| Context window | 400K | 1M |
| Input $/Mtok | $1.25 | $0.065 |
| Output $/Mtok | $10.00 | $0.26 |
| Coding index | 15.3 | 41.1 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.892 | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex better than Qwen3.5-Flash for coding?
Pick Qwen3.5-Flash for coding: it scores 41.1 on the Vibe Coding Index against 15.3 for GPT-5.1-Codex. Pick GPT-5.1-Codex for work measured by LMArena WebDev or LiveCodeBench (Vals AI), where it leads.
Which is cheaper, GPT-5.1-Codex or Qwen3.5-Flash?
GPT-5.1-Codex costs $1.25 per million input tokens and $10.00 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Per solved SWE-bench Verified task, GPT-5.1-Codex costs $0.892 and Qwen3.5-Flash $0.40.
Which has the bigger context window, GPT-5.1-Codex or Qwen3.5-Flash?
Qwen3.5-Flash, with 1M tokens against 400K.
How many benchmarks is the GPT-5.1-Codex vs Qwen3.5-Flash comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. GPT-5.1-Codex's coding index rests on 2 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.