GPT-5.2-Codex vs Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads GPT-5.2-Codex on the Artificial Analysis Intelligence Index, 39.9 to 28.5, and Qwen3.8 2.4T A95B costs 2.3x less per output token ($6.00 vs $14.00 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
GPT-5.2-Codex or Qwen3.8 2.4T A95B: which to pick
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Qwen3.8 2.4T A95B has none, so the two cannot be ranked on coding.
Pick Qwen3.8 2.4T A95B for output tokens 2.3x cheaper ($6.00 vs $14.00 per million) and a larger context window (1.05M against 400K tokens).
- Shared results
- 4
- GPT-5.2-Codex only
- 18
- Qwen3.8 2.4T A95B only
- 3
- Like-for-like categories
- 3 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 GPT-5.2-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads, 39.9 against 28.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.8 2.4T A95B has a AA Agentic Index result, 50.1.
Directional only
Long documents
Listed context window
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 2.4T A95B
GPT-5.2-Codex $0.0088, Qwen3.8 2.4T A95B $0.0050 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, Qwen3.8 2.4T A95B not benchmarked.
- Widest category
- Intelligence, 11.4 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: GPT-5.2-Codex 28.5 against Qwen3.8 2.4T A95B 39.9, 11.4 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2-Codex $14.00 against Qwen3.8 2.4T A95B $6.00 per million output tokens, 2.3x apart.
- Context window
- GPT-5.2-Codex takes 400K tokens, Qwen3.8 2.4T A95B 1.05M.
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 | Qwen3.8 2.4T A95B | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 1 | 0 | ||
| All shared boards | 1 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index13 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3.8 2.4T A95B | 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 |
| AA Coding Index | Not measured | 71.9 | Only Qwen3.8 2.4T A95B 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 |
| SciCode | Not measured | 54.1% | Only Qwen3.8 2.4T A95B 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 | Not measured | Only GPT-5.2-Codex measured |
| 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 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.2-Codex | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | 39.9 | Qwen3.8 2.4T A95B |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 50.1 | Only Qwen3.8 2.4T A95B measured |
| 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 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.2-Codex | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 89.9% | 93.5% | Qwen3.8 2.4T A95B |
| Humanity's Last Exam (Artificial Analysis run) | 35.7% | 42.4% | Qwen3.8 2.4T A95B |
| Neither model has a result on the other 20 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3.8 2.4T A95B | 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-5.2-Codex | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| Neither model has a result on 18 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| IFBench | 77.6% | Not measured | Only GPT-5.2-Codex measured |
| Long context reasoning (AA-LCR) | 82.3% | 80.3% | GPT-5.2-Codex |
| 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-5.2-Codex | Qwen3.8 2.4T A95B | 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
Qwen3.8 2.4T A95B costs less
Repository review
50K input + 3K output tokens
Qwen3.8 2.4T A95B costs less
Agent loop
200K input + 10K output tokens
Qwen3.8 2.4T A95B 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 | Qwen3.8 2.4T A95B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | January 14, 2026 | August 12, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.75 | $2.00 |
| Output $/Mtok | $14.00 | $6.00 |
| 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 Qwen3.8 2.4T A95B for coding?
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Qwen3.8 2.4T A95B has none, so the two cannot be ranked on coding. Pick Qwen3.8 2.4T A95B for output tokens 2.3x cheaper ($6.00 vs $14.00 per million) and a larger context window (1.05M against 400K tokens).
Which is cheaper, GPT-5.2-Codex or Qwen3.8 2.4T A95B?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3.8 2.4T A95B costs $2.00 and $6.00. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GPT-5.2-Codex or Qwen3.8 2.4T A95B?
Qwen3.8 2.4T A95B, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.2-Codex vs Qwen3.8 2.4T A95B comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. GPT-5.2-Codex's coding index rests on 3 independent benchmarks and Qwen3.8 2.4T A95B's on 0. Where only one model has a result, the page says so instead of naming a winner.