GPT-4o (2024-05-13) vs Qwen2.5 Coder 32B Instruct
GPT-4o (2024-05-13) leads Qwen2.5 Coder 32B Instruct on the Artificial Analysis Intelligence Index, 7.3 to 6.7, and Qwen2.5 Coder 32B Instruct costs 15.0x less per output token ($1.00 vs $15.00 per million). Updated October 22, 2026; 28 benchmarks have a result for both.
GPT-4o (2024-05-13) or Qwen2.5 Coder 32B 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) and work measured by Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 28
- GPT-4o (2024-05-13) only
- 16
- Qwen2.5 Coder 32B Instruct only
- 7
- Like-for-like categories
- 6 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
GPT-4o (2024-05-13)
GPT-4o (2024-05-13) leads, 7.3 against 6.7.
Same board, both measured
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 Coder 32B Instruct
GPT-4o (2024-05-13) $0.013, Qwen2.5 Coder 32B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- GPT-4o (2024-05-13) not benchmarked, Qwen2.5 Coder 32B Instruct not benchmarked.
- Widest category
- General, 13.0 points apart on average over 11 shared benchmarks.
- Widest single gap
- LMArena Text: entertainment and media: GPT-4o (2024-05-13) 1289 against Qwen2.5 Coder 32B Instruct 1150, 23.2 points apart on a 0 to 100 scale.
- Output price
- GPT-4o (2024-05-13) $15.00 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 15.0x apart.
- Context window
- GPT-4o (2024-05-13) takes 128K tokens, Qwen2.5 Coder 32B 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 Coder 32B Instruct | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 4 | 1 | ||
| Math | 2 | 0 | ||
| Knowledge | 6 | 0 | ||
| General | 11 | 0 | ||
| All shared boards | 27 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | GPT-4o (2024-05-13) | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 16.4% | Only Qwen2.5 Coder 32B Instruct measured |
| SWE-bench Verified | Not measured | 47.0% | Only Qwen2.5 Coder 32B Instruct measured |
| AA Coding Index | 24.2 | Not measured | Only GPT-4o (2024-05-13) measured |
| LiveCodeBench (Artificial Analysis run) | 33.4% | 29.5% | GPT-4o (2024-05-13) |
| LMArena Text: coding | 1297 | 1276 | GPT-4o (2024-05-13) |
| LMArena Text: software and IT | 1297 | 1261 | GPT-4o (2024-05-13) |
| Neither model has a result on the other 37 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4o (2024-05-13) | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.3 | 6.7 | GPT-4o (2024-05-13) |
| Epoch Capabilities Index | Not measured | 119.5 | Only Qwen2.5 Coder 32B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| METR time horizons | 40.8% | Not measured | Only GPT-4o (2024-05-13) measured |
| BALROG | 32.3% | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GPT-4o (2024-05-13) | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 50.2% | Not measured | Only GPT-4o (2024-05-13) measured |
| GPQA Diamond (Artificial Analysis run) | 52.6% | 41.7% | GPT-4o (2024-05-13) |
| Humanity's Last Exam (Artificial Analysis run) | 1.8% | 3.5% | Qwen2.5 Coder 32B Instruct |
| GPQA Diamond (Epoch AI run) | 48.9% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: hard prompts | 1281 | 1251 | GPT-4o (2024-05-13) |
| LMArena Text: expert | 1250 | 1221 | GPT-4o (2024-05-13) |
| LMArena Text: hard prompts in English | 1293 | 1269 | GPT-4o (2024-05-13) |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-4o (2024-05-13) | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| OTIS Mock AIME | 6.3% | Not measured | Only GPT-4o (2024-05-13) measured |
| MATH level 5 | 51.0% | Not measured | Only GPT-4o (2024-05-13) measured |
| GSM8K | Not measured | 93.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | 1284 | 1251 | GPT-4o (2024-05-13) |
| LMArena Text: mathematical work | 1284 | 1243 | GPT-4o (2024-05-13) |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-4o (2024-05-13) | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.0% | 63.5% | GPT-4o (2024-05-13) |
| MMLU | 84.2% | 79.1% | GPT-4o (2024-05-13) |
| AI2 Reasoning Challenge | Not measured | 70.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | 1295 | 1221 | GPT-4o (2024-05-13) |
| LMArena Text: medicine and healthcare | 1278 | 1187 | GPT-4o (2024-05-13) |
| LMArena Text: legal and government | 1314 | 1257 | GPT-4o (2024-05-13) |
| LMArena Text: business and finance | 1262 | 1224 | GPT-4o (2024-05-13) |
| Neither model has a result on the other 13 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 Coder 32B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | GPT-4o (2024-05-13) | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| HellaSwag | Not measured | 83.0% | Only Qwen2.5 Coder 32B Instruct measured |
| WinoGrande | Not measured | 80.8% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text | 1300 | 1230 | GPT-4o (2024-05-13) |
| LMArena Text: instruction following | 1278 | 1223 | GPT-4o (2024-05-13) |
| LMArena Text: creative writing | 1291 | 1174 | GPT-4o (2024-05-13) |
| LMArena Vision | 1137 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: English | 1312 | 1248 | GPT-4o (2024-05-13) |
| LMArena Text: non-English | 1283 | 1205 | GPT-4o (2024-05-13) |
| LMArena Text: Chinese | 1277 | 1222 | GPT-4o (2024-05-13) |
| 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 | 1228 | GPT-4o (2024-05-13) |
| LMArena Text: Polish | 1365 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: multi-turn | 1302 | 1222 | GPT-4o (2024-05-13) |
| LMArena Text: longer queries | 1288 | 1251 | GPT-4o (2024-05-13) |
| LMArena Text: writing and language | 1298 | 1191 | GPT-4o (2024-05-13) |
| LMArena Text: entertainment and media | 1289 | 1150 | GPT-4o (2024-05-13) |
| 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 Coder 32B 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 Coder 32B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 Coder 32B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 Coder 32B 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 Coder 32B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | May 13, 2024 | November 11, 2024 |
| Context window | 128K | 33K |
| Input $/Mtok | $5.00 | $0.66 |
| Output $/Mtok | $15.00 | $1.00 |
| 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 Coder 32B 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) and work measured by Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, GPT-4o (2024-05-13) or Qwen2.5 Coder 32B Instruct?
GPT-4o (2024-05-13) costs $5.00 per million input tokens and $15.00 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4o (2024-05-13) or Qwen2.5 Coder 32B Instruct?
GPT-4o (2024-05-13), with 128K tokens against 33K.
How many benchmarks is the GPT-4o (2024-05-13) vs Qwen2.5 Coder 32B Instruct comparison based on?
28 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 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.