o3 vs Qwen3 30B A3B
o3 leads Qwen3 30B A3B on the Artificial Analysis Intelligence Index, 20.2 to 7.6, and Qwen3 30B A3B costs 16.0x less per output token ($0.50 vs $8.00 per million). Updated October 21, 2026; 52 benchmarks have a result for both.
o3 or Qwen3 30B A3B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick o3 for a larger context window (200K against 41K tokens).
- Shared results
- 52
- o3 only
- 50
- Qwen3 30B A3B only
- 0
- Like-for-like categories
- 8 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
o3
o3 leads, 20.2 against 7.6.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only o3 has a GPQA Diamond result, 84.1%.
Directional only
Math
AIME
No call
Only o3 has a AIME result, 85.3%.
Directional only
Knowledge
MMLU Pro
No call
Only o3 has a MMLU Pro result, 85.6%.
Directional only
Web design
Design Arena: websites
o3
o3 leads, 1036 against 955.
Same board, both measured
Long documents
Listed context window
o3
o3 takes 200K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 30B A3B
o3 $0.0060, Qwen3 30B A3B $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only o3 has the number, $0.571.
Directional only
At a glance
- Coding index
- o3 not benchmarked, Qwen3 30B A3B not benchmarked.
- Widest category
- Agentic work, 54.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): o3 74.7% against Qwen3 30B A3B 0.0%, 74.7 points apart on a 0 to 100 scale.
- Output price
- o3 $8.00 against Qwen3 30B A3B $0.50 per million output tokens, 16.0x apart.
- Context window
- o3 takes 200K 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 | o3 | Bars | Qwen3 30B A3B | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 5 | 0 | ||
| Knowledge | 5 | 0 | ||
| Web design | 5 | 0 | ||
| General | 23 | 0 | ||
| All shared boards | 52 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | o3 | Qwen3 30B A3B | Leader |
|---|---|---|---|
| Aider polyglot | 81.3% | Not measured | Only o3 measured |
| SWE-bench Verified | 58.4% | Not measured | Only o3 measured |
| SWE-bench Multimodal | 36.0% | Not measured | Only o3 measured |
| LiveCodeBench (Vals AI) | 83.9% | Not measured | Only o3 measured |
| LiveCodeBench (Artificial Analysis run) | 80.8% | 50.6% | o3 |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | 6.8% | o3 |
| GSO-Bench | 8.8% | Not measured | Only o3 measured |
| WeirdML | 52.4% | 29.8% | o3 |
| ALE-Bench | 933.5 | Not measured | Only o3 measured |
| LMArena Text: coding | 1407 | 1337 | o3 |
| LMArena Text: software and IT | 1415 | 1340 | o3 |
| SciCode | 9.2% | Not measured | Only o3 measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | o3 | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.2 | 7.6 | o3 |
| Epoch Capabilities Index | 146.7 | 137.3 | o3 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | o3 | Qwen3 30B A3B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 80.7% | 26.0% | o3 |
| METR time horizons | 65.4% | Not measured | Only o3 measured |
| GDPval | 30.8% | Not measured | Only o3 measured |
| DeepResearch Bench | 45.2% | Not measured | Only o3 measured |
| OSWorld | 23.0% | Not measured | Only o3 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 63.0% | Not measured | Only o3 measured |
| BrowseComp-Plus | 65.9% | Not measured | Only o3 measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | o3 | Qwen3 30B A3B | Leader |
|---|---|---|---|
| GPQA Diamond | 84.1% | Not measured | Only o3 measured |
| MMMU Pro (Vals AI) | 80.4% | Not measured | Only o3 measured |
| GPQA Diamond (Artificial Analysis run) | 82.7% | 61.6% | o3 |
| Humanity's Last Exam (Artificial Analysis run) | 20.1% | 6.2% | o3 |
| GPQA Diamond (Epoch AI run) | 81.8% | 61.7% | o3 |
| ARC-AGI | 60.8% | Not measured | Only o3 measured |
| ARC-AGI-2 | 6.5% | Not measured | Only o3 measured |
| SimpleBench | 53.1% | Not measured | Only o3 measured |
| Chess puzzles | 37.0% | Not measured | Only o3 measured |
| Mystery game puzzles | 29.0% | Not measured | Only o3 measured |
| VPCT | 52.0% | Not measured | Only o3 measured |
| LMArena Text: hard prompts | 1402 | 1314 | o3 |
| LMArena Text: expert | 1402 | 1314 | o3 |
| LMArena Text: hard prompts in English | 1411 | 1329 | o3 |
| LMArena Vision: diagrams | 1220 | Not measured | Only o3 measured |
| LMArena Vision: homework | 1234 | Not measured | Only o3 measured |
| Kagi LLM Benchmark | 67.6% | Not measured | Only o3 measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | o3 | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AIME | 85.3% | Not measured | Only o3 measured |
| MATH 500 (Vals AI) | 94.6% | Not measured | Only o3 measured |
| MGSM (Vals AI) | 91.8% | Not measured | Only o3 measured |
| AA Math Index | 88.3 | 72.3 | o3 |
| AIME 2025 (Artificial Analysis run) | 88.3% | 72.3% | o3 |
| OTIS Mock AIME | 84.4% | 62.8% | o3 |
| FrontierMath, tiers 1 to 3 | 33.3% | Not measured | Only o3 measured |
| MATH level 5 | 97.8% | Not measured | Only o3 measured |
| LMArena Text: math | 1426 | 1355 | o3 |
| LMArena Text: mathematical work | 1423 | 1363 | o3 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | o3 | Qwen3 30B A3B | Leader |
|---|---|---|---|
| MMLU Pro | 85.6% | Not measured | Only o3 measured |
| LegalBench (Vals AI) | 83.8% | Not measured | Only o3 measured |
| CorpFin (Vals AI) | 59.7% | Not measured | Only o3 measured |
| TaxEval (Vals AI) | 74.6% | Not measured | Only o3 measured |
| MortgageTax (Vals AI) | 65.7% | Not measured | Only o3 measured |
| MedQA (Vals AI) | 96.1% | Not measured | Only o3 measured |
| MedCode (Vals AI) | 47.3% | Not measured | Only o3 measured |
| MMLU Pro (Artificial Analysis run) | 85.3% | 77.7% | o3 |
| SimpleQA Verified | 49.4% | Not measured | Only o3 measured |
| GeoBench | 74.0% | Not measured | Only o3 measured |
| LMArena Text: science | 1429 | 1321 | o3 |
| LMArena Text: medicine and healthcare | 1448 | 1318 | o3 |
| LMArena Text: legal and government | 1428 | 1314 | o3 |
| LMArena Text: business and finance | 1393 | 1309 | o3 |
| FACTS Benchmark Suite | 44.4% | Not measured | Only o3 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | o3 | Qwen3 30B A3B | Leader |
|---|---|---|---|
| Design Arena: websites | 1036 | 955 | o3 |
| Design Arena: UI components | 1017 | 946 | o3 |
| Design Arena: game dev | 1034 | 899 | o3 |
| Design Arena: data viz | 1200 | 967 | o3 |
| Design Arena: all code categories | 1020 | 942 | o3 |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index230
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o3 | 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 o3 | Qwen3 30B A3B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | April 16, 2025 | April 28, 2025 |
| Context window | 200K | 41K |
| Input $/Mtok | $2.00 | $0.12 |
| Output $/Mtok | $8.00 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | $0.571 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is o3 better than Qwen3 30B A3B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick o3 for a larger context window (200K against 41K tokens).
Which is cheaper, o3 or Qwen3 30B A3B?
o3 costs $2.00 per million input tokens and $8.00 per million output tokens; Qwen3 30B A3B costs $0.12 and $0.50. Cost per solved task is known for o3 only.
Which has the bigger context window, o3 or Qwen3 30B A3B?
o3, with 200K tokens against 41K.
How many benchmarks is the o3 vs Qwen3 30B A3B comparison based on?
52 benchmarks have a result for both models as of October 21, 2026. o3'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.