GPT-4o vs Qwen2.5 7B Instruct
Qwen2.5 7B Instruct costs 50.0x less per output token than GPT-4o ($0.20 vs $10.00 per million). Updated October 21, 2026; 3 benchmarks have a result for both.
GPT-4o or Qwen2.5 7B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4o for a larger context window (128K against 33K tokens).
- Shared results
- 3
- GPT-4o only
- 24
- Qwen2.5 7B Instruct only
- 4
- Like-for-like categories
- 2 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
Neither has a coding score yet.
One side not measured
Web design
Design Arena: websites
No call
Only GPT-4o has a Design Arena: websites result, 831.
Directional only
Long documents
Listed context window
GPT-4o
GPT-4o takes 128K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 7B Instruct
GPT-4o $0.0075, Qwen2.5 7B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-4o not benchmarked, Qwen2.5 7B Instruct not benchmarked.
- Widest category
- Knowledge, 15.2 points apart on average over 1 shared benchmark.
- Widest single gap
- DTBench: GPT-4o 64.5% against Qwen2.5 7B Instruct 47.7%, 16.8 points apart on a 0 to 100 scale.
- Output price
- GPT-4o $10.00 against Qwen2.5 7B Instruct $0.20 per million output tokens, 50.0x apart.
- Context window
- GPT-4o takes 128K 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-4o | Bars | Qwen2.5 7B Instruct | Ties |
|---|---|---|---|---|
| Knowledge | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 3 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | GPT-4o | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| SWE-bench Verified | 38.8% | Not measured | Only GPT-4o measured |
| SWE-bench Lite | 38.0% | Not measured | Only GPT-4o measured |
| SWE-bench (full test set) | 18.8% | Not measured | Only GPT-4o measured |
| SWE-bench Multimodal | 30.4% | Not measured | Only GPT-4o measured |
| LiveCodeBench (Artificial Analysis run) | 30.9% | Not measured | Only GPT-4o measured |
| Terminal-Bench Hard (Artificial Analysis run) | 8.3% | Not measured | Only GPT-4o measured |
| SciCode | 1.5% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-4o | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Epoch Capabilities Index | Not measured | 118.5 | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-4o | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 25.1% | Not measured | Only GPT-4o measured |
| GDPval | 9.9% | Not measured | Only GPT-4o measured |
| BALROG | Not measured | 7.8% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-4o | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 54.3% | Not measured | Only GPT-4o measured |
| Humanity's Last Exam (Artificial Analysis run) | 2.4% | Not measured | Only GPT-4o measured |
| GPQA Diamond (Epoch AI run) | Not measured | 35.5% | Only Qwen2.5 7B Instruct measured |
| ARC-AGI | 4.5% | Not measured | Only GPT-4o measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-4o | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 6.0 | Not measured | Only GPT-4o measured |
| AIME 2025 (Artificial Analysis run) | 6.0% | Not measured | Only GPT-4o measured |
| OTIS Mock AIME | Not measured | 2.5% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4o | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.8% | Not measured | Only GPT-4o measured |
| MMLU | 88.1% | 72.9% | GPT-4o |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4o | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 831 | Not measured | Only GPT-4o measured |
| Design Arena: UI components | 885 | Not measured | Only GPT-4o measured |
| Design Arena: game dev | 906 | Not measured | Only GPT-4o measured |
| Design Arena: data viz | 858 | Not measured | Only GPT-4o measured |
| Design Arena: 3D | 869 | Not measured | Only GPT-4o measured |
| Design Arena: all code categories | 861 | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-4o | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| IFBench | 34.3% | Not measured | Only GPT-4o measured |
| Long context reasoning (AA-LCR) | 49.3% | Not measured | Only GPT-4o measured |
| DTBench | 64.5% | 47.7% | GPT-4o |
| LMCA | 16.6% | 6.4% | GPT-4o |
| 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-4o | 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-4o | Qwen2.5 7B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | May 13, 2024 | October 16, 2024 |
| Context window | 128K | 33K |
| Input $/Mtok | $2.50 | $0.10 |
| Output $/Mtok | $10.00 | $0.20 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4o better than Qwen2.5 7B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o for a larger context window (128K against 33K tokens).
Which is cheaper, GPT-4o or Qwen2.5 7B Instruct?
GPT-4o costs $2.50 per million input tokens and $10.00 per million output tokens; Qwen2.5 7B Instruct costs $0.10 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4o or Qwen2.5 7B Instruct?
GPT-4o, with 128K tokens against 33K.
How many benchmarks is the GPT-4o vs Qwen2.5 7B Instruct comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. GPT-4o's coding index rests on 0 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.