GPT-4o-mini vs Qwen3 14B
GPT-4o-mini and Qwen3 14B tie on the Artificial Analysis Intelligence Index at 6.7, and Qwen3 14B costs 2.6x less per output token ($0.23 vs $0.60 per million). Updated October 21, 2026; 12 benchmarks have a result for both.
GPT-4o-mini or Qwen3 14B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4o-mini 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
- 12
- GPT-4o-mini only
- 17
- Qwen3 14B only
- 8
- 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
No call
Tied at 6.7.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-4o-mini has a GPQA Diamond result, 44.2%.
Directional only
Math
AIME
No call
Only GPT-4o-mini has a AIME result, 11.5%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4o-mini has a MMLU Pro result, 62.7%.
Directional only
Long documents
Listed context window
GPT-4o-mini
GPT-4o-mini takes 128K tokens against 41K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 14B
GPT-4o-mini $0.0004, Qwen3 14B $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-4o-mini not benchmarked, Qwen3 14B not benchmarked.
- Widest category
- Math, 43.3 points apart on average over 2 shared benchmarks.
- Widest single gap
- AIME 2025 (Artificial Analysis run): GPT-4o-mini 14.7% against Qwen3 14B 58.0%, 43.3 points apart on a 0 to 100 scale.
- Output price
- GPT-4o-mini $0.60 against Qwen3 14B $0.23 per million output tokens, 2.6x apart.
- Context window
- GPT-4o-mini 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-4o-mini | Bars | Qwen3 14B | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | 1 tie | |
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 3 | ||
| All shared boards | 0 | 11 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GPT-4o-mini | Qwen3 14B | Leader |
|---|---|---|---|
| AA Coding Index | 11.4 | Not measured | Only GPT-4o-mini measured |
| LiveCodeBench (Vals AI) | 26.4% | Not measured | Only GPT-4o-mini measured |
| LiveCodeBench (Artificial Analysis run) | 23.4% | 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 |
| SciCode | 1.5% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o-mini | Qwen3 14B | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 6.7 | Tie |
| Epoch Capabilities Index | 126.5 | 138.2 | Qwen3 14B |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o-mini | 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 index03
| Benchmark | GPT-4o-mini | Qwen3 14B | Leader |
|---|---|---|---|
| GPQA Diamond | 44.2% | Not measured | Only GPT-4o-mini measured |
| MMMU Pro (Vals AI) | 56.6% | Not measured | Only GPT-4o-mini measured |
| GPQA Diamond (Artificial Analysis run) | 42.6% | 60.4% | Qwen3 14B |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | 4.5% | Qwen3 14B |
| GPQA Diamond (Epoch AI run) | Not measured | 63.8% | Only Qwen3 14B measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o-mini measured |
| Kagi LLM Benchmark | 28.8% | 49.1% | Qwen3 14B |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o-mini | Qwen3 14B | Leader |
|---|---|---|---|
| AIME | 11.5% | Not measured | Only GPT-4o-mini measured |
| MATH 500 (Vals AI) | 72.6% | Not measured | Only GPT-4o-mini measured |
| MGSM (Vals AI) | 86.2% | Not measured | Only GPT-4o-mini measured |
| AA Math Index | 14.7 | 58.0 | Qwen3 14B |
| AIME 2025 (Artificial Analysis run) | 14.7% | 58.0% | Qwen3 14B |
| OTIS Mock AIME | Not measured | 66.4% | Only Qwen3 14B measured |
| GSM8K | 91.3% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o-mini | Qwen3 14B | Leader |
|---|---|---|---|
| MMLU Pro | 62.7% | Not measured | Only GPT-4o-mini measured |
| CorpFin (Vals AI) | 45.5% | Not measured | Only GPT-4o-mini measured |
| TaxEval (Vals AI) | 60.5% | Not measured | Only GPT-4o-mini measured |
| MortgageTax (Vals AI) | 54.5% | Not measured | Only GPT-4o-mini measured |
| MedQA (Vals AI) | 72.4% | Not measured | Only GPT-4o-mini measured |
| MMLU Pro (Artificial Analysis run) | 64.8% | 77.4% | Qwen3 14B |
| MMLU | 81.8% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 11 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini | Qwen3 14B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o-mini | Qwen3 14B | Leader |
|---|---|---|---|
| IFBench | 31.0% | 40.5% | Qwen3 14B |
| Long context reasoning (AA-LCR) | Not measured | 0.0% | Only Qwen3 14B measured |
| DTBench | 54.4% | 64.0% | Qwen3 14B |
| LMCA | 10.4% | 18.2% | Qwen3 14B |
| Fiction.LiveBench | Not measured | 62.5% | Only Qwen3 14B measured |
| PIQA | 88.7% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 40 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini | 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-4o-mini | Qwen3 14B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | July 18, 2024 | April 28, 2025 |
| Context window | 128K | 41K |
| Input $/Mtok | $0.15 | $0.11 |
| Output $/Mtok | $0.60 | $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-4o-mini better than Qwen3 14B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o-mini 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-4o-mini or Qwen3 14B?
GPT-4o-mini costs $0.15 per million input tokens and $0.60 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-4o-mini or Qwen3 14B?
GPT-4o-mini, with 128K tokens against 41K.
How many benchmarks is the GPT-4o-mini vs Qwen3 14B comparison based on?
12 benchmarks have a result for both models as of October 21, 2026. GPT-4o-mini'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.