GPT-5.3-Codex vs Qwen2.5 7B Instruct
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-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Qwen2.5 7B Instruct has none, so the two cannot be ranked on coding.
Qwen2.5 7B Instruct has the edge where output tokens cost 70.0x less.
- Shared results
- 1
- GPT-5.3-Codex only
- 33
- Qwen2.5 7B Instruct only
- 6
- 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-5.3-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.3-Codex has a AA Intelligence Index result, 32.5.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5.3-Codex has a Design Arena: websites result, 1163.
Directional only
Long documents
Listed context window
GPT-5.3-Codex
GPT-5.3-Codex takes 400K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 7B Instruct
GPT-5.3-Codex $0.0088, Qwen2.5 7B Instruct $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.3-Codex has the number, $0.59.
Directional only
At a glance
- Coding index
- GPT-5.3-Codex 60.0 from 3 benchmarks, Qwen2.5 7B Instruct not benchmarked.
- Widest category
- Intelligence, 48.2 points apart on average over 1 shared benchmark.
- Widest single gap
- Epoch Capabilities Index: GPT-5.3-Codex 157.1 against Qwen2.5 7B Instruct 118.5, 48.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against Qwen2.5 7B Instruct $0.20 per million output tokens, 70.0x apart.
- Context window
- GPT-5.3-Codex takes 400K tokens, Qwen2.5 7B Instruct 33K.
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-5.3-Codex | Bars | Qwen2.5 7B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index12 boards, none shared
| Benchmark | GPT-5.3-Codex | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| LMArena WebDev | 1407 | Not measured | Only GPT-5.3-Codex measured |
| LiveCodeBench (Vals AI) | 87.3% | Not measured | Only GPT-5.3-Codex measured |
| SWE-bench Verified (Vals AI) | 78.0% | Not measured | Only GPT-5.3-Codex measured |
| Vibe Code Bench (Vals AI) | 61.8% | Not measured | Only GPT-5.3-Codex measured |
| IOI (Vals AI) | 53.8% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench | 78.4% | Not measured | Only GPT-5.3-Codex measured |
| WeirdML | 79.3% | Not measured | Only GPT-5.3-Codex measured |
| ALE-Bench | 1655.2 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: React | 1395 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: HTML | 1422 | Not measured | Only GPT-5.3-Codex measured |
| LMArena WebDev: from an image | 1443 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.3-Codex | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | Not measured | Only GPT-5.3-Codex measured |
| Epoch Capabilities Index | 157.1 | 118.5 | GPT-5.3-Codex |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.3-Codex | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1032 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | 983 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | 1053 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | 1124 | Not measured | Only GPT-5.3-Codex measured |
| τ²-Bench Telecom | 86.0% | Not measured | Only GPT-5.3-Codex measured |
| METR time horizons | 74.5% | Not measured | Only GPT-5.3-Codex measured |
| BALROG | Not measured | 7.8% | Only Qwen2.5 7B Instruct measured |
| Vending-Bench 2 | $5,940 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.3-Codex | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 91.5% | Not measured | Only GPT-5.3-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | Not measured | Only GPT-5.3-Codex measured |
| GPQA Diamond (Epoch AI run) | Not measured | 35.5% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.3-Codex | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 2.5% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.3-Codex | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| MMLU | Not measured | 72.9% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.3-Codex | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1163 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: UI components | 1137 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: game dev | 1161 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: data viz | 1163 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: 3D | 1008 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: all code categories | 1145 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: SVG | 1129 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: ASCII art | 1151 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.3-Codex | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| IFBench | 75.4% | Not measured | Only GPT-5.3-Codex measured |
| Long context reasoning (AA-LCR) | 83.3% | Not measured | Only GPT-5.3-Codex measured |
| DTBench | Not measured | 47.7% | Only Qwen2.5 7B Instruct measured |
| LMCA | Not measured | 6.4% | Only Qwen2.5 7B Instruct 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-5.3-Codex | Qwen2.5 7B Instruct | 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
Qwen2.5 7B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 7B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 7B 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-5.3-Codex | Qwen2.5 7B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | February 24, 2026 | October 16, 2024 |
| Context window | 400K | 33K |
| Input $/Mtok | $1.75 | $0.10 |
| Output $/Mtok | $14.00 | $0.20 |
| Coding index | 60.0 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.59 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than Qwen2.5 7B Instruct for coding?
Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Qwen2.5 7B Instruct has none, so the two cannot be ranked on coding. Qwen2.5 7B Instruct has the edge where output tokens cost 70.0x less.
Which is cheaper, GPT-5.3-Codex or Qwen2.5 7B Instruct?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen2.5 7B Instruct costs $0.10 and $0.20. Cost per solved task is known for GPT-5.3-Codex only.
Which has the bigger context window, GPT-5.3-Codex or Qwen2.5 7B Instruct?
GPT-5.3-Codex, with 400K tokens against 33K.
How many benchmarks is the GPT-5.3-Codex vs Qwen2.5 7B Instruct comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. GPT-5.3-Codex's coding index rests on 3 independent benchmarks and Qwen2.5 7B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.