GPT-5.2-Codex vs Qwen2.5 VL 72B Instruct
Qwen2.5 VL 72B Instruct costs 14.0x less per output token than GPT-5.2-Codex ($1.00 vs $14.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- 45.4
- Overall
- No data
- Output $/M
- $14.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.00
GPT-5.2-Codex or Qwen2.5 VL 72B Instruct: which to pick
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Qwen2.5 VL 72B Instruct has none, so the two cannot be ranked on coding.
Pick Qwen2.5 VL 72B Instruct for output tokens 14.0x cheaper ($1.00 vs $14.00 per million).
No benchmark has tested both yet. GPT-5.2-Codex has results on SWE-bench Verified, SWE-bench Multilingual and LMArena WebDev; Qwen2.5 VL 72B Instruct on OSWorld, Kagi LLM Benchmark and GeoBench. Compare them on price and context instead.
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
GPT-5.2-Codex
GPT-5.2-Codex takes 400K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 VL 72B Instruct
GPT-5.2-Codex $0.0088, Qwen2.5 VL 72B Instruct $0.0013 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, Qwen2.5 VL 72B Instruct not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GPT-5.2-Codex $14.00 against Qwen2.5 VL 72B Instruct $1.00 per million output tokens, 14.0x apart.
- Context window
- GPT-5.2-Codex takes 400K tokens, Qwen2.5 VL 72B Instruct 128K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index11 boards measured
| Benchmark | GPT-5.2-Codex | Qwen2.5 VL 72B Instruct | 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 | 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 28 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.2-Codex | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | GPT-5.2-Codex | Qwen2.5 VL 72B Instruct | 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 | 1090 | 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 |
| OSWorld | Not measured | 5.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | GPT-5.2-Codex | Qwen2.5 VL 72B Instruct | 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 |
| Kagi LLM Benchmark | Not measured | 36.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 19 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.2-Codex | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| GeoBench | Not measured | 62.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 17 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GPT-5.2-Codex | Qwen2.5 VL 72B Instruct | 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 |
| LMArena Vision | Not measured | 1107 | Only Qwen2.5 VL 72B Instruct measured |
| LMArena Vision: English | Not measured | 1115 | Only Qwen2.5 VL 72B Instruct measured |
| LMArena Vision: Chinese | Not measured | 1107 | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 41 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
Qwen2.5 VL 72B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 VL 72B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 VL 72B Instruct 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 | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | January 14, 2026 | February 1, 2025 |
| Context window | 400K | 128K |
| Input $/Mtok | $1.75 | $0.80 |
| Output $/Mtok | $14.00 | $1.00 |
| 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 Qwen2.5 VL 72B Instruct for coding?
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Qwen2.5 VL 72B Instruct has none, so the two cannot be ranked on coding. Pick Qwen2.5 VL 72B Instruct for output tokens 14.0x cheaper ($1.00 vs $14.00 per million).
Which is cheaper, GPT-5.2-Codex or Qwen2.5 VL 72B Instruct?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen2.5 VL 72B Instruct costs $0.80 and $1.00. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GPT-5.2-Codex or Qwen2.5 VL 72B Instruct?
GPT-5.2-Codex, with 400K tokens against 128K.
Has any benchmark tested both GPT-5.2-Codex and Qwen2.5 VL 72B Instruct?
No benchmark has tested both yet. GPT-5.2-Codex has results on SWE-bench Verified, SWE-bench Multilingual and LMArena WebDev; Qwen2.5 VL 72B Instruct on OSWorld, Kagi LLM Benchmark and GeoBench. Compare them on price and context instead. Data as of October 21, 2026.