GPT-4 Turbo vs Qwen3 14B
GPT-4 Turbo leads Qwen3 14B on the Artificial Analysis Intelligence Index, 7.0 to 6.7, and Qwen3 14B costs 130.4x less per output token ($0.23 vs $30.00 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
GPT-4 Turbo or Qwen3 14B: 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 Epoch Capabilities Index, where it leads.
- Shared results
- 7
- GPT-4 Turbo only
- 35
- Qwen3 14B only
- 13
- Like-for-like categories
- 5 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-4 Turbo
GPT-4 Turbo leads, 7.0 against 6.7.
Same board, both measured
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 14B
GPT-4 Turbo $0.025, Qwen3 14B $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-4 Turbo not benchmarked, Qwen3 14B not benchmarked.
- Widest category
- Coding, 23.2 points apart on average over 1 shared benchmark.
- Widest single gap
- LiveCodeBench (Artificial Analysis run): GPT-4 Turbo 29.1% against Qwen3 14B 52.3%, 23.2 points apart on a 0 to 100 scale.
- Output price
- GPT-4 Turbo $30.00 against Qwen3 14B $0.23 per million output tokens, 130.4x apart.
- Context window
- GPT-4 Turbo takes 128K tokens, Qwen3 14B 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 14B | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 1 | 1 | ||
| Reasoning | 0 | 1 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 2 | ||
| All shared boards | 1 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GPT-4 Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| AA Coding Index | 21.5 | Not measured | Only GPT-4 Turbo measured |
| LiveCodeBench (Artificial Analysis run) | 29.1% | 52.3% | Qwen3 14B |
| SciCode | Not measured | 30.7% | Only Qwen3 14B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 5.3% | Only Qwen3 14B measured |
| LMArena Text: coding | 1268 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: software and IT | 1267 | Not measured | Only GPT-4 Turbo measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-4 Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.0 | 6.7 | GPT-4 Turbo |
| Epoch Capabilities Index | 127.3 | 138.2 | Qwen3 14B |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4 Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 34.5% | Only Qwen3 14B measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 41.0% | Only Qwen3 14B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4 Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 60.4% | Only Qwen3 14B measured |
| Humanity's Last Exam (Artificial Analysis run) | 3.1% | 4.5% | Qwen3 14B |
| GPQA Diamond (Epoch AI run) | Not measured | 63.8% | Only Qwen3 14B measured |
| LMArena Text: hard prompts | 1251 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: expert | 1223 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: hard prompts in English | 1266 | Not measured | Only GPT-4 Turbo measured |
| Kagi LLM Benchmark | Not measured | 49.1% | Only Qwen3 14B measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-4 Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 58.0 | Only Qwen3 14B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 58.0% | Only Qwen3 14B measured |
| OTIS Mock AIME | Not measured | 66.4% | Only Qwen3 14B measured |
| LMArena Text: math | 1272 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: mathematical work | 1268 | Not measured | Only GPT-4 Turbo 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-4 Turbo | Qwen3 14B | 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% | 77.4% | Qwen3 14B |
| MMLU | 81.3% | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: science | 1256 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: medicine and healthcare | 1239 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: legal and government | 1265 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: business and finance | 1222 | Not measured | Only GPT-4 Turbo measured |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4 Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4 Turbo | Qwen3 14B | Leader |
|---|---|---|---|
| IFBench | Not measured | 40.5% | Only Qwen3 14B measured |
| Long context reasoning (AA-LCR) | Not measured | 0.0% | Only Qwen3 14B measured |
| DTBench | 61.6% | 64.0% | Qwen3 14B |
| LMCA | 9.8% | 18.2% | Qwen3 14B |
| Fiction.LiveBench | Not measured | 62.5% | Only Qwen3 14B measured |
| LMArena Text | 1272 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: instruction following | 1249 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: creative writing | 1268 | Not measured | Only GPT-4 Turbo measured |
| LMArena Vision | 1090 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: English | 1291 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: non-English | 1245 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Chinese | 1242 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: German | 1259 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Japanese | 1194 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Korean | 1187 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: French | 1276 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Spanish | 1260 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Russian | 1259 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Polish | 1302 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: multi-turn | 1267 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: longer queries | 1254 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: writing and language | 1279 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: entertainment and media | 1258 | Not measured | Only GPT-4 Turbo measured |
| 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 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4 Turbo | Qwen3 14B | 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 14B costs less
Repository review
50K input + 3K output tokens
Qwen3 14B costs less
Agent loop
200K input + 10K output tokens
Qwen3 14B 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 14B |
|---|---|---|
| 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 | $0.11 |
| Output $/Mtok | $30.00 | $0.23 |
| 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 14B 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 Epoch Capabilities Index, where it leads.
Which is cheaper, GPT-4 Turbo or Qwen3 14B?
GPT-4 Turbo costs $10.00 per million input tokens and $30.00 per million output tokens; Qwen3 14B costs $0.11 and $0.23. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4 Turbo or Qwen3 14B?
GPT-4 Turbo, with 128K tokens against 41K.
How many benchmarks is the GPT-4 Turbo vs Qwen3 14B comparison based on?
7 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 14B's on 0. Where only one model has a result, the page says so instead of naming a winner.