GPT-4 Turbo vs Qwen3 30B A3B
Qwen3 30B A3B leads GPT-4 Turbo on the Artificial Analysis Intelligence Index, 7.6 to 7.0, and Qwen3 30B A3B costs 60.0x less per output token ($0.50 vs $30.00 per million). Updated October 22, 2026; 35 benchmarks have a result for both.
GPT-4 Turbo or Qwen3 30B A3B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4 Turbo for a larger context window (128K against 41K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 35
- GPT-4 Turbo only
- 7
- Qwen3 30B A3B only
- 18
- 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
Qwen3 30B A3B leads, 7.6 against 7.0.
Same board, both measured
Web design
Design Arena: websites
No call
Only Qwen3 30B A3B has a Design Arena: websites result, 954.
Directional only
Long documents
Listed context window
GPT-4 Turbo
GPT-4 Turbo takes 128K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 30B A3B
GPT-4 Turbo $0.025, Qwen3 30B A3B $0.0004 per turn.
List prices
At a glance
- Coding index
- GPT-4 Turbo not benchmarked, Qwen3 30B A3B not benchmarked.
- Widest category
- Coding, 15.0 points apart on average over 3 shared benchmarks.
- Widest single gap
- LiveCodeBench (Artificial Analysis run): GPT-4 Turbo 29.1% against Qwen3 30B A3B 50.6%, 21.5 points apart on a 0 to 100 scale.
- Output price
- GPT-4 Turbo $30.00 against Qwen3 30B A3B $0.50 per million output tokens, 60.0x apart.
- Context window
- GPT-4 Turbo 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-4 Turbo | Bars | Qwen3 30B A3B | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 5 | ||
| General | 2 | 17 | ||
| All shared boards | 2 | 33 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GPT-4 Turbo | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Coding Index | 21.5 | Not measured | Only GPT-4 Turbo measured |
| LiveCodeBench (Artificial Analysis run) | 29.1% | 50.6% | Qwen3 30B A3B |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 6.8% | Only Qwen3 30B A3B measured |
| WeirdML | Not measured | 29.8% | Only Qwen3 30B A3B measured |
| LMArena Text: coding | 1268 | 1337 | Qwen3 30B A3B |
| LMArena Text: software and IT | 1267 | 1340 | Qwen3 30B A3B |
| Neither model has a result on the other 37 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4 Turbo | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.0 | 7.6 | Qwen3 30B A3B |
| Epoch Capabilities Index | 127.3 | 137.3 | Qwen3 30B A3B |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-4 Turbo | Qwen3 30B A3B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 26.0% | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4 Turbo | Qwen3 30B A3B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 61.6% | Only Qwen3 30B A3B measured |
| Humanity's Last Exam (Artificial Analysis run) | 3.1% | 6.2% | Qwen3 30B A3B |
| GPQA Diamond (Epoch AI run) | Not measured | 61.7% | Only Qwen3 30B A3B measured |
| LMArena Text: hard prompts | 1251 | 1314 | Qwen3 30B A3B |
| LMArena Text: expert | 1223 | 1314 | Qwen3 30B A3B |
| LMArena Text: hard prompts in English | 1266 | 1329 | Qwen3 30B A3B |
| Neither model has a result on the other 17 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4 Turbo | Qwen3 30B A3B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 72.3 | Only Qwen3 30B A3B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 72.3% | Only Qwen3 30B A3B measured |
| OTIS Mock AIME | Not measured | 62.8% | Only Qwen3 30B A3B measured |
| LMArena Text: math | 1272 | 1355 | Qwen3 30B A3B |
| LMArena Text: mathematical work | 1268 | 1363 | Qwen3 30B A3B |
| MathArena | Not measured | 9.4% | 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 index05
| Benchmark | GPT-4 Turbo | Qwen3 30B A3B | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 80.5% | Not measured | Only GPT-4 Turbo measured |
| MedQA (Vals AI) | 82.0% | Not measured | Only GPT-4 Turbo measured |
| MMLU Pro (Artificial Analysis run) | 69.4% | 77.7% | Qwen3 30B A3B |
| MMLU | 81.3% | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: science | 1256 | 1321 | Qwen3 30B A3B |
| LMArena Text: medicine and healthcare | 1239 | 1318 | Qwen3 30B A3B |
| LMArena Text: legal and government | 1265 | 1314 | Qwen3 30B A3B |
| LMArena Text: business and finance | 1222 | 1309 | Qwen3 30B A3B |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-4 Turbo | Qwen3 30B A3B | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 954 | Only Qwen3 30B A3B measured |
| Design Arena: UI components | Not measured | 943 | Only Qwen3 30B A3B measured |
| Design Arena: game dev | Not measured | 898 | Only Qwen3 30B A3B measured |
| Design Arena: data viz | Not measured | 967 | Only Qwen3 30B A3B measured |
| Design Arena: all code categories | Not measured | 941 | Only Qwen3 30B A3B measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index217
| Benchmark | GPT-4 Turbo | Qwen3 30B A3B | Leader |
|---|---|---|---|
| IFBench | Not measured | 41.5% | Only Qwen3 30B A3B measured |
| Long context reasoning (AA-LCR) | Not measured | 0.0% | Only Qwen3 30B A3B measured |
| DTBench | 61.6% | 60.3% | GPT-4 Turbo |
| LMCA | 9.8% | 15.8% | Qwen3 30B A3B |
| 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 | 1272 | 1317 | Qwen3 30B A3B |
| LMArena Text: instruction following | 1249 | 1283 | Qwen3 30B A3B |
| LMArena Text: creative writing | 1268 | 1271 | Qwen3 30B A3B |
| LMArena Vision | 1090 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: English | 1291 | 1335 | Qwen3 30B A3B |
| LMArena Text: non-English | 1245 | 1295 | Qwen3 30B A3B |
| LMArena Text: Chinese | 1242 | 1347 | Qwen3 30B A3B |
| LMArena Text: German | 1259 | 1307 | Qwen3 30B A3B |
| LMArena Text: Japanese | 1194 | 1254 | Qwen3 30B A3B |
| LMArena Text: Korean | 1187 | 1261 | Qwen3 30B A3B |
| LMArena Text: French | 1276 | 1352 | Qwen3 30B A3B |
| LMArena Text: Spanish | 1260 | 1317 | Qwen3 30B A3B |
| LMArena Text: Russian | 1259 | 1291 | Qwen3 30B A3B |
| LMArena Text: Polish | 1302 | 1299 | GPT-4 Turbo |
| LMArena Text: multi-turn | 1267 | 1307 | Qwen3 30B A3B |
| LMArena Text: longer queries | 1254 | 1312 | Qwen3 30B A3B |
| LMArena Text: writing and language | 1279 | 1290 | Qwen3 30B A3B |
| LMArena Text: entertainment and media | 1258 | 1258 | Qwen3 30B A3B |
| LMArena Vision: English | 1097 | Not measured | Only GPT-4 Turbo measured |
| LMArena Vision: Chinese | 1045 | Not measured | Only GPT-4 Turbo measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4 Turbo | 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-4 Turbo | Qwen3 30B A3B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | April 9, 2024 | April 28, 2025 |
| Context window | 128K | 41K |
| Input $/Mtok | $10.00 | $0.12 |
| Output $/Mtok | $30.00 | $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-4 Turbo better than Qwen3 30B A3B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4 Turbo for a larger context window (128K against 41K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, GPT-4 Turbo or Qwen3 30B A3B?
GPT-4 Turbo costs $10.00 per million input tokens and $30.00 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-4 Turbo or Qwen3 30B A3B?
GPT-4 Turbo, with 128K tokens against 41K.
How many benchmarks is the GPT-4 Turbo vs Qwen3 30B A3B comparison based on?
35 benchmarks have a result for both models as of October 22, 2026. GPT-4 Turbo'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.