GPT-5.2-Codex vs Qwen3.6 Flash
Updated October 21, 2026. 1 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Qwen3.6 Flash has none, so the two cannot be ranked on coding.
Qwen3.6 Flash has the edge where output tokens cost 12.4x less, and where the context window is larger, 1M against 400K tokens.
- Shared results
- 1
- GPT-5.2-Codex only
- 21
- Qwen3.6 Flash only
- 10
- Like-for-like categories
- 1 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
Only GPT-5.2-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.2-Codex has a AA Intelligence Index result, 28.5.
Directional only
Long documents
Listed context window
Qwen3.6 Flash
Qwen3.6 Flash takes 1M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 Flash
GPT-5.2-Codex $0.0088, Qwen3.6 Flash $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2-Codex has the number, $0.43.
Directional only
At a glance
- Coding index
- GPT-5.2-Codex 45.4 from 3 benchmarks, Qwen3.6 Flash not benchmarked.
- Widest category
- Coding, 32.5 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: GPT-5.2-Codex 1299.9 against Qwen3.6 Flash 326.4, 32.5 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2-Codex $14.00 against Qwen3.6 Flash $1.125 per million output tokens, 12.4x apart.
- Context window
- GPT-5.2-Codex takes 400K tokens, Qwen3.6 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-5.2-Codex | Bars | Qwen3.6 Flash | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-5.2-Codex | Qwen3.6 Flash | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Multilingual | 66.3% | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev | 1339 | Not measured | Only GPT-5.2-Codex measured |
| LiveCodeBench (Vals AI) | 88.0% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Verified (Vals AI) | 72.4% | Not measured | Only GPT-5.2-Codex measured |
| Vibe Code Bench (Vals AI) | 37.9% | Not measured | Only GPT-5.2-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | Not measured | Only GPT-5.2-Codex measured |
| Terminal-Bench | 66.5% | Not measured | Only GPT-5.2-Codex measured |
| ALE-Bench | 1299.9 | 326.4 | GPT-5.2-Codex |
| LMArena WebDev: React | 1329 | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev: HTML | 1348 | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3.6 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | Not measured | Only GPT-5.2-Codex measured |
| Epoch Capabilities Index | Not measured | 142.9 | Only Qwen3.6 Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3.6 Flash | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1051 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | 993 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | 1089 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | 1176 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2-Codex measured |
| τ²-Bench Telecom | 92.1% | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3.6 Flash | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 89.9% | Not measured | Only GPT-5.2-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 35.7% | Not measured | Only GPT-5.2-Codex measured |
| GPQA Diamond (Epoch AI run) | Not measured | 83.3% | Only Qwen3.6 Flash measured |
| SimpleBench | Not measured | 35.2% | Only Qwen3.6 Flash measured |
| Chess puzzles | Not measured | 12.0% | Only Qwen3.6 Flash measured |
| Mystery game puzzles | Not measured | 18.0% | Only Qwen3.6 Flash measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3.6 Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 84.4% | Only Qwen3.6 Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 22.5% | Only Qwen3.6 Flash 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-5.2-Codex | Qwen3.6 Flash | Leader |
|---|---|---|---|
| SimpleQA Verified | Not measured | 15.9% | Only Qwen3.6 Flash measured |
| Neither model has a result on the other 16 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3.6 Flash | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3.6 Flash | Leader |
|---|---|---|---|
| IFBench | 77.6% | Not measured | Only GPT-5.2-Codex measured |
| Long context reasoning (AA-LCR) | 82.3% | Not measured | Only GPT-5.2-Codex measured |
| DTBench | Not measured | 77.1% | Only Qwen3.6 Flash measured |
| LMCA | Not measured | 31.0% | Only Qwen3.6 Flash measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3.6 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
Qwen3.6 Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.6 Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.6 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-5.2-Codex | Qwen3.6 Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | January 14, 2026 | April 27, 2026 |
| Context window | 400K | 1M |
| Input $/Mtok | $1.75 | $0.1875 |
| Output $/Mtok | $14.00 | $1.125 |
| Coding index | 45.4 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.43 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2-Codex better than Qwen3.6 Flash for coding?
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Qwen3.6 Flash has none, so the two cannot be ranked on coding. Qwen3.6 Flash has the edge where output tokens cost 12.4x less, and where the context window is larger, 1M against 400K tokens.
Which is cheaper, GPT-5.2-Codex or Qwen3.6 Flash?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3.6 Flash costs $0.1875 and $1.125. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GPT-5.2-Codex or Qwen3.6 Flash?
Qwen3.6 Flash, with 1M tokens against 400K.
How many benchmarks is the GPT-5.2-Codex vs Qwen3.6 Flash comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. GPT-5.2-Codex's coding index rests on 3 independent benchmarks and Qwen3.6 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.