GPT-4o vs Qwen3 30B A3B
Qwen3 30B A3B costs 20.0x less per output token than GPT-4o ($0.50 vs $10.00 per million). Updated October 21, 2026; 17 benchmarks have a result for both.
GPT-4o or Qwen3 30B A3B: 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 41K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or τ²-Bench Telecom, where it leads.
- Shared results
- 17
- GPT-4o only
- 10
- Qwen3 30B A3B only
- 35
- Like-for-like categories
- 7 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 Qwen3 30B A3B has a AA Intelligence Index result, 7.6.
Directional only
Web design
Design Arena: websites
Qwen3 30B A3B
Qwen3 30B A3B leads, 955 against 831.
Same board, both measured
Long documents
Listed context window
GPT-4o
GPT-4o takes 128K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 30B A3B
GPT-4o $0.0075, Qwen3 30B A3B $0.0004 per turn.
List prices
At a glance
- Coding index
- GPT-4o not benchmarked, Qwen3 30B A3B not benchmarked.
- Widest category
- Math, 66.3 points apart on average over 2 shared benchmarks.
- Widest single gap
- AIME 2025 (Artificial Analysis run): GPT-4o 6.0% against Qwen3 30B A3B 72.3%, 66.3 points apart on a 0 to 100 scale.
- Output price
- GPT-4o $10.00 against Qwen3 30B A3B $0.50 per million output tokens, 20.0x apart.
- Context window
- GPT-4o takes 128K tokens, Qwen3 30B A3B 41K.
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 | Qwen3 30B A3B | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 1 | ||
| Web design | 1 | 4 | ||
| General | 3 | 1 | ||
| All shared boards | 5 | 12 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
| Benchmark | GPT-4o | Qwen3 30B A3B | 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% | 50.6% | Qwen3 30B A3B |
| Terminal-Bench Hard (Artificial Analysis run) | 8.3% | 6.8% | GPT-4o |
| WeirdML | Not measured | 29.8% | Only Qwen3 30B A3B measured |
| LMArena Text: coding | Not measured | 1337 | Only Qwen3 30B A3B measured |
| LMArena Text: software and IT | Not measured | 1340 | Only Qwen3 30B A3B measured |
| SciCode | 1.5% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 7.6 | Only Qwen3 30B A3B measured |
| Epoch Capabilities Index | Not measured | 137.3 | Only Qwen3 30B A3B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o | Qwen3 30B A3B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 25.1% | 26.0% | Qwen3 30B A3B |
| GDPval | 9.9% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o | Qwen3 30B A3B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 54.3% | 61.6% | Qwen3 30B A3B |
| Humanity's Last Exam (Artificial Analysis run) | 2.4% | 6.2% | Qwen3 30B A3B |
| GPQA Diamond (Epoch AI run) | Not measured | 61.7% | Only Qwen3 30B A3B measured |
| ARC-AGI | 4.5% | Not measured | Only GPT-4o measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o measured |
| LMArena Text: hard prompts | Not measured | 1314 | Only Qwen3 30B A3B measured |
| LMArena Text: expert | Not measured | 1314 | Only Qwen3 30B A3B measured |
| LMArena Text: hard prompts in English | Not measured | 1329 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Math Index | 6.0 | 72.3 | Qwen3 30B A3B |
| AIME 2025 (Artificial Analysis run) | 6.0% | 72.3% | Qwen3 30B A3B |
| OTIS Mock AIME | Not measured | 62.8% | Only Qwen3 30B A3B measured |
| LMArena Text: math | Not measured | 1355 | Only Qwen3 30B A3B measured |
| LMArena Text: mathematical work | Not measured | 1363 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o | Qwen3 30B A3B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.8% | 77.7% | Qwen3 30B A3B |
| MMLU | 88.1% | Not measured | Only GPT-4o measured |
| LMArena Text: science | Not measured | 1321 | Only Qwen3 30B A3B measured |
| LMArena Text: medicine and healthcare | Not measured | 1318 | Only Qwen3 30B A3B measured |
| LMArena Text: legal and government | Not measured | 1314 | Only Qwen3 30B A3B measured |
| LMArena Text: business and finance | Not measured | 1309 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 12 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | GPT-4o | Qwen3 30B A3B | Leader |
|---|---|---|---|
| Design Arena: websites | 831 | 955 | Qwen3 30B A3B |
| Design Arena: UI components | 885 | 946 | Qwen3 30B A3B |
| Design Arena: game dev | 906 | 899 | GPT-4o |
| Design Arena: data viz | 858 | 967 | Qwen3 30B A3B |
| Design Arena: 3D | 869 | Not measured | Only GPT-4o measured |
| Design Arena: all code categories | 861 | 942 | Qwen3 30B A3B |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GPT-4o | Qwen3 30B A3B | Leader |
|---|---|---|---|
| IFBench | 34.3% | 41.5% | Qwen3 30B A3B |
| Long context reasoning (AA-LCR) | 49.3% | 0.0% | GPT-4o |
| DTBench | 64.5% | 60.3% | GPT-4o |
| LMCA | 16.6% | 15.8% | GPT-4o |
| Fiction.LiveBench | Not measured | 40.6% | Only Qwen3 30B A3B measured |
| Creative writing (Lech Mazur) | Not measured | 75.3% | Only Qwen3 30B A3B measured |
| LMArena Text | Not measured | 1317 | Only Qwen3 30B A3B measured |
| LMArena Text: instruction following | Not measured | 1283 | Only Qwen3 30B A3B measured |
| LMArena Text: creative writing | Not measured | 1271 | Only Qwen3 30B A3B measured |
| LMArena Text: English | Not measured | 1335 | Only Qwen3 30B A3B measured |
| LMArena Text: non-English | Not measured | 1295 | Only Qwen3 30B A3B measured |
| LMArena Text: Chinese | Not measured | 1347 | Only Qwen3 30B A3B measured |
| LMArena Text: German | Not measured | 1307 | Only Qwen3 30B A3B measured |
| LMArena Text: Japanese | Not measured | 1254 | Only Qwen3 30B A3B measured |
| LMArena Text: Korean | Not measured | 1261 | Only Qwen3 30B A3B measured |
| LMArena Text: French | Not measured | 1352 | Only Qwen3 30B A3B measured |
| LMArena Text: Spanish | Not measured | 1317 | Only Qwen3 30B A3B measured |
| LMArena Text: Russian | Not measured | 1291 | Only Qwen3 30B A3B measured |
| LMArena Text: Polish | Not measured | 1299 | Only Qwen3 30B A3B measured |
| LMArena Text: multi-turn | Not measured | 1307 | Only Qwen3 30B A3B measured |
| LMArena Text: longer queries | Not measured | 1312 | Only Qwen3 30B A3B measured |
| LMArena Text: writing and language | Not measured | 1290 | Only Qwen3 30B A3B measured |
| LMArena Text: entertainment and media | Not measured | 1258 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o | Qwen3 30B A3B | 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 costs less
Repository review
50K input + 3K output tokens
Qwen3 30B A3B costs less
Agent loop
200K input + 10K output tokens
Qwen3 30B A3B 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 | Qwen3 30B A3B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | May 13, 2024 | April 28, 2025 |
| Context window | 128K | 41K |
| Input $/Mtok | $2.50 | $0.12 |
| Output $/Mtok | $10.00 | $0.50 |
| 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 Qwen3 30B A3B 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 41K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or τ²-Bench Telecom, where it leads.
Which is cheaper, GPT-4o or Qwen3 30B A3B?
GPT-4o costs $2.50 per million input tokens and $10.00 per million output tokens; Qwen3 30B A3B costs $0.12 and $0.50. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4o or Qwen3 30B A3B?
GPT-4o, with 128K tokens against 41K.
How many benchmarks is the GPT-4o vs Qwen3 30B A3B comparison based on?
17 benchmarks have a result for both models as of October 21, 2026. GPT-4o's coding index rests on 0 independent benchmarks and Qwen3 30B A3B's on 0. Where only one model has a result, the page says so instead of naming a winner.