GPT-4o (2024-05-13) vs Qwen2.5 7B Instruct
Qwen2.5 7B Instruct costs 75.0x less per output token than GPT-4o (2024-05-13) ($0.20 vs $15.00 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
GPT-4o (2024-05-13) 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 (2024-05-13) for a larger context window (128K against 33K tokens).
- Shared results
- 4
- GPT-4o (2024-05-13) only
- 40
- Qwen2.5 7B Instruct only
- 3
- Like-for-like categories
- 4 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
Intelligence
AA Intelligence Index
No call
Only GPT-4o (2024-05-13) has a AA Intelligence Index result, 7.3.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-4o (2024-05-13) has a GPQA Diamond result, 50.2%.
Directional only
Long documents
Listed context window
GPT-4o (2024-05-13)
GPT-4o (2024-05-13) takes 128K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 7B Instruct
GPT-4o (2024-05-13) $0.013, Qwen2.5 7B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-4o (2024-05-13) not benchmarked, Qwen2.5 7B Instruct not benchmarked.
- Widest category
- Agentic work, 24.5 points apart on average over 1 shared benchmark.
- Widest single gap
- BALROG: GPT-4o (2024-05-13) 32.3% against Qwen2.5 7B Instruct 7.8%, 24.5 points apart on a 0 to 100 scale.
- Output price
- GPT-4o (2024-05-13) $15.00 against Qwen2.5 7B Instruct $0.20 per million output tokens, 75.0x apart.
- Context window
- GPT-4o (2024-05-13) 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 (2024-05-13) | Bars | Qwen2.5 7B Instruct | Ties |
|---|---|---|---|---|
| Agentic work | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| Math | 1 | 0 | ||
| Knowledge | 1 | 0 | ||
| All shared boards | 4 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | 24.2 | Not measured | Only GPT-4o (2024-05-13) measured |
| LiveCodeBench (Artificial Analysis run) | 33.4% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: coding | 1297 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: software and IT | 1297 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 39 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.3 | Not measured | Only GPT-4o (2024-05-13) measured |
| Epoch Capabilities Index | Not measured | 118.5 | Only Qwen2.5 7B Instruct measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4o (2024-05-13) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| METR time horizons | 40.8% | Not measured | Only GPT-4o (2024-05-13) measured |
| BALROG | 32.3% | 7.8% | GPT-4o (2024-05-13) |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4o (2024-05-13) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 50.2% | Not measured | Only GPT-4o (2024-05-13) measured |
| GPQA Diamond (Artificial Analysis run) | 52.6% | Not measured | Only GPT-4o (2024-05-13) measured |
| Humanity's Last Exam (Artificial Analysis run) | 1.8% | Not measured | Only GPT-4o (2024-05-13) measured |
| GPQA Diamond (Epoch AI run) | 48.9% | 35.5% | GPT-4o (2024-05-13) |
| LMArena Text: hard prompts | 1281 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: expert | 1250 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: hard prompts in English | 1293 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4o (2024-05-13) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| OTIS Mock AIME | 6.3% | 2.5% | GPT-4o (2024-05-13) |
| MATH level 5 | 51.0% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: math | 1284 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: mathematical work | 1284 | Not measured | Only GPT-4o (2024-05-13) 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 (2024-05-13) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.0% | Not measured | Only GPT-4o (2024-05-13) measured |
| MMLU | 84.2% | 72.9% | GPT-4o (2024-05-13) |
| LMArena Text: science | 1295 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: medicine and healthcare | 1278 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: legal and government | 1314 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: business and finance | 1262 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 14 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index22 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| DTBench | Not measured | 47.7% | Only Qwen2.5 7B Instruct measured |
| LMCA | Not measured | 6.4% | Only Qwen2.5 7B Instruct measured |
| LMArena Text | 1300 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: instruction following | 1278 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: creative writing | 1291 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Vision | 1137 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: English | 1312 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: non-English | 1283 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Chinese | 1277 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: German | 1283 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Japanese | 1257 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Korean | 1234 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: French | 1304 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Spanish | 1292 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Russian | 1285 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: Polish | 1365 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: multi-turn | 1302 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: longer queries | 1288 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: writing and language | 1298 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: entertainment and media | 1289 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Vision: English | 1147 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Vision: Chinese | 1132 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | 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 (2024-05-13) | 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 | $5.00 | $0.10 |
| Output $/Mtok | $15.00 | $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 (2024-05-13) 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 (2024-05-13) for a larger context window (128K against 33K tokens).
Which is cheaper, GPT-4o (2024-05-13) or Qwen2.5 7B Instruct?
GPT-4o (2024-05-13) costs $5.00 per million input tokens and $15.00 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 (2024-05-13) or Qwen2.5 7B Instruct?
GPT-4o (2024-05-13), with 128K tokens against 33K.
How many benchmarks is the GPT-4o (2024-05-13) vs Qwen2.5 7B Instruct comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. GPT-4o (2024-05-13)'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.