GPT-4o (2024-05-13) vs Qwen3 30B A3B Instruct 2507
Qwen3 30B A3B Instruct 2507 leads GPT-4o (2024-05-13) on the Artificial Analysis Intelligence Index, 7.5 to 7.3, and Qwen3 30B A3B Instruct 2507 costs 50.0x less per output token ($0.30 vs $15.00 per million). Updated October 23, 2026; 35 benchmarks have a result for both.
GPT-4o (2024-05-13) or Qwen3 30B A3B Instruct 2507: 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 work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 35
- GPT-4o (2024-05-13) only
- 9
- Qwen3 30B A3B Instruct 2507 only
- 10
- 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
Qwen3 30B A3B Instruct 2507
Qwen3 30B A3B Instruct 2507 leads, 7.5 against 7.3.
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
Qwen3 30B A3B Instruct 2507
Qwen3 30B A3B Instruct 2507 takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 30B A3B Instruct 2507
GPT-4o (2024-05-13) $0.013, Qwen3 30B A3B Instruct 2507 $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-4o (2024-05-13) not benchmarked, Qwen3 30B A3B Instruct 2507 not benchmarked.
- Widest category
- Math, 31.8 points apart on average over 3 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: GPT-4o (2024-05-13) 6.3% against Qwen3 30B A3B Instruct 2507 62.2%, 56.0 points apart on a 0 to 100 scale.
- Output price
- GPT-4o (2024-05-13) $15.00 against Qwen3 30B A3B Instruct 2507 $0.30 per million output tokens, 50.0x apart.
- Context window
- GPT-4o (2024-05-13) takes 128K tokens, Qwen3 30B A3B Instruct 2507 262K.
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 | Qwen3 30B A3B Instruct 2507 | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 5 | ||
| General | 1 | 16 | ||
| All shared boards | 1 | 34 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GPT-4o (2024-05-13) | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| AA Coding Index | 24.2 | Not measured | Only GPT-4o (2024-05-13) measured |
| LiveCodeBench (Artificial Analysis run) | 33.4% | 70.7% | Qwen3 30B A3B Instruct 2507 |
| SciCode | Not measured | 33.0% | Only Qwen3 30B A3B Instruct 2507 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 6.1% | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: coding | 1297 | 1416 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: software and IT | 1297 | 1417 | Qwen3 30B A3B Instruct 2507 |
| Neither model has a result on the other 37 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o (2024-05-13) | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.3 | 7.5 | Qwen3 30B A3B Instruct 2507 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 28.1% | Only Qwen3 30B A3B Instruct 2507 measured |
| 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 |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 41.4% | Only Qwen3 30B A3B Instruct 2507 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GPT-4o (2024-05-13) | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| GPQA Diamond | 50.2% | Not measured | Only GPT-4o (2024-05-13) measured |
| GPQA Diamond (Artificial Analysis run) | 52.6% | 70.7% | Qwen3 30B A3B Instruct 2507 |
| Humanity's Last Exam (Artificial Analysis run) | 1.8% | 10.3% | Qwen3 30B A3B Instruct 2507 |
| GPQA Diamond (Epoch AI run) | 48.9% | 55.6% | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: hard prompts | 1281 | 1398 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: expert | 1250 | 1396 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: hard prompts in English | 1293 | 1406 | Qwen3 30B A3B Instruct 2507 |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o (2024-05-13) | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 66.3 | Only Qwen3 30B A3B Instruct 2507 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 66.3% | Only Qwen3 30B A3B Instruct 2507 measured |
| OTIS Mock AIME | 6.3% | 62.2% | Qwen3 30B A3B Instruct 2507 |
| MATH level 5 | 51.0% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: math | 1284 | 1395 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: mathematical work | 1284 | 1412 | Qwen3 30B A3B Instruct 2507 |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GPT-4o (2024-05-13) | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.0% | 80.5% | Qwen3 30B A3B Instruct 2507 |
| MMLU | 84.2% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: science | 1295 | 1393 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: medicine and healthcare | 1278 | 1392 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: legal and government | 1314 | 1387 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: business and finance | 1261 | 1389 | Qwen3 30B A3B Instruct 2507 |
| 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) | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index116
| Benchmark | GPT-4o (2024-05-13) | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| IFBench | Not measured | 50.7% | Only Qwen3 30B A3B Instruct 2507 measured |
| Long context reasoning (AA-LCR) | Not measured | 61.3% | Only Qwen3 30B A3B Instruct 2507 measured |
| DTBench | Not measured | 67.2% | Only Qwen3 30B A3B Instruct 2507 measured |
| LMCA | Not measured | 22.4% | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text | 1300 | 1384 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: instruction following | 1278 | 1363 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: creative writing | 1291 | 1317 | Qwen3 30B A3B Instruct 2507 |
| LMArena Vision | 1137 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: English | 1312 | 1391 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: non-English | 1283 | 1372 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: Chinese | 1277 | 1433 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: German | 1282 | 1379 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: Japanese | 1257 | 1338 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: Korean | 1234 | 1331 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: French | 1304 | 1419 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: Spanish | 1292 | 1404 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: Russian | 1285 | 1369 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: Polish | 1365 | 1353 | GPT-4o (2024-05-13) |
| LMArena Text: multi-turn | 1302 | 1378 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: longer queries | 1288 | 1378 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: writing and language | 1298 | 1336 | Qwen3 30B A3B Instruct 2507 |
| LMArena Text: entertainment and media | 1289 | 1334 | Qwen3 30B A3B Instruct 2507 |
| 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 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Qwen3 30B A3B Instruct 2507 | 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
Qwen3 30B A3B Instruct 2507 costs less
Repository review
50K input + 3K output tokens
Qwen3 30B A3B Instruct 2507 costs less
Agent loop
200K input + 10K output tokens
Qwen3 30B A3B Instruct 2507 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) | Qwen3 30B A3B Instruct 2507 |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | May 13, 2024 | July 29, 2025 |
| Context window | 128K | 262K |
| Input $/Mtok | $5.00 | $0.09 |
| Output $/Mtok | $15.00 | $0.30 |
| 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 Qwen3 30B A3B Instruct 2507 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o (2024-05-13) for work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, GPT-4o (2024-05-13) or Qwen3 30B A3B Instruct 2507?
GPT-4o (2024-05-13) costs $5.00 per million input tokens and $15.00 per million output tokens; Qwen3 30B A3B Instruct 2507 costs $0.09 and $0.30. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4o (2024-05-13) or Qwen3 30B A3B Instruct 2507?
Qwen3 30B A3B Instruct 2507, with 262K tokens against 128K.
How many benchmarks is the GPT-4o (2024-05-13) vs Qwen3 30B A3B Instruct 2507 comparison based on?
35 benchmarks have a result for both models as of October 23, 2026. GPT-4o (2024-05-13)'s coding index rests on 0 independent benchmarks and Qwen3 30B A3B Instruct 2507's on 0. Where only one model has a result, the page says so instead of naming a winner.