qwen3-14b: benchmark scores, price and ranking
qwen3-14b from Alibaba ranks #573 of 743 LLMs on the LLMs Tiger aggregate benchmark score (45.6), based on 9 benchmark results captured up to 2026-10-13.
- Organisation
- Alibaba
- Type
- Open-weight / local model
- Open weights
- Yes
- Released
- 2025-04-28 (source: epoch)
- Cheapest API price
- $0.080 in / $0.240 out per million tokens (via nebius, observed 2026-10-13T19:47Z)
- Aggregate rank
- #573 of 743 (score 45.6)
Relative to other models, qwen3-14b is strongest on Epoch ECI (#60 of 204) and weakest on CritPt (#111 of 115).
Across the 9 benchmarks where at least ten models are scored, qwen3-14b is in the top 10% on 0 and in the top half on 3.
Its cheapest listed input price, $0.080 per million tokens, is lower than that of 85% of the 427 models with a listed price.
qwen3-14b benchmark results
| Benchmark | Score | Rank | Source | Captured |
|---|---|---|---|---|
| Epoch ECI | 138 | #60 of 204 | Epoch AI Benchmarking Hub | 2026-10-13 |
| OTIS Mock AIME | 66.4% | #87 of 189 | Epoch AI Benchmarking Hub | 2026-10-13 |
| Fiction.liveBench | 62.5% | #22 of 47 | Epoch AI Benchmarking Hub | 2026-10-13 |
| DTBench | 64.0% | #86 of 146 | Epoch AI Benchmarking Hub | 2026-10-13 |
| GPQA Diamond | 63.8% | #162 of 275 | Epoch AI Benchmarking Hub | 2026-10-13 |
| Chess Puzzles | 4.0% | #84 of 122 | Epoch AI Benchmarking Hub | 2026-10-13 |
| LMCA | 18.2% | #76 of 108 | Epoch AI Benchmarking Hub | 2026-10-13 |
| SciCode | 31.6% | #90 of 106 | Epoch AI Benchmarking Hub | 2026-10-13 |
| CritPt | 0.0% | #111 of 115 | Epoch AI Benchmarking Hub | 2026-10-13 |
About qwen3-14b
qwen3-14b is an open-weight model that can run on your own hardware from Alibaba. It was released on 2025-04-28, according to Epoch AI. The cheapest API listing we track charges $0.080 per million input tokens and $0.240 per million output tokens, through nebius. We hold 9 benchmark results for it across 4 areas, and none is in the top tenth of its board.
Where qwen3-14b stands
On our aggregate score, qwen3-14b is #573 of 743 ranked models, in the lower half. Its score is 45.6; the scale is centred on 50, so it sits 4.4 points below the typical model. That beats 23% of the ranked field. Among the 74 ranked Alibaba models it is #60. The next one up is qwen-plus-2025-01-25 at 45.7. The next one down is qwen3-coder-next at 45.5.
qwen3-14b, benchmark by benchmark
Epoch ECI puts qwen3-14b at 138, #60 of 204. That beats 71% of the models tested, but 18 points short of Qwen 3.8 Max (156). The results either side are grok-3 (138) and QwQ-32B (138). It covers Epoch Capabilities Index: Epoch's own aggregate of ~40 benchmarks. The board median is 127, so qwen3-14b is 11 points above it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
On OTIS Mock AIME, qwen3-14b reaches 66.4%. It ranks #87 of 189, ahead of 54% of the field, and 33.6 points behind Claude Fable 5.1 (max) at 100.0%. The results either side are DeepSeek-R1 (May 2025) (66.4%) and Claude Opus 4 (64.4%). It covers competition maths (AIME 2024-25 style). The board median is 57.8%, so qwen3-14b is 8.6 points above it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
qwen3-14b manages 62.5% on Fiction.liveBench, good for #22 of 47. grok-4-0709 leads at 94.4%, 31.9 points further up, and 53% of models sit below. The results either side are GPT-4.5 Preview (Feb 2025) (63.9%) and qwen3-8b (62.1%). It covers long-context story comprehension (16k). The board median is 61.1%, so qwen3-14b is 1.4 points above it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
qwen3-14b lands below the middle on DTBench: 64.0%, #86 of 146, with 58% of the field scoring higher. Claude Opus 5.5 (max) leads at 98.9%; the closest result above is GPT-4o (May 2024) at 64.5%. It covers dynamic tool-use benchmark. The board median is 68.8%, so qwen3-14b is 4.8 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
qwen3-14b lands below the middle on GPQA Diamond: 63.8%, #162 of 275, with 59% of the field scoring higher. GPT-6 Astra (max) leads at 95.8%; the closest result above is Gemini 2.0 Flash (Feb 2025) at 64.1%. It covers expert-level science questions. The board median is 71.7%, so qwen3-14b is 8.0 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
qwen3-14b lands below the middle on Chess Puzzles: 4.0%, #84 of 122, with 68% of the field scoring higher. GPT-6 Astra (max) leads at 72.0%; the closest result above is GPT-4 (Jun 2023) at 4.0%. It covers chess tactics puzzles. The board median is 12.5%, so qwen3-14b is 8.5 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
qwen3-14b lands below the middle on LMCA: 18.2%, #76 of 108, with 69% of the field scoring higher. Claude Opus 5.5 (max) leads at 68.2%; the closest result above is o3-mini (high) at 19.0%. It covers language-model capability assessment. The board median is 29.2%, so qwen3-14b is 11.0 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
SciCode is a weak spot. qwen3-14b scores 31.6%, #90 of 106, behind 84% of the models measured. The leader, Claude Opus 5.5 (max), reaches 66.9%. It covers scientific research code. The board median is 44.3%, so qwen3-14b is 12.7 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
CritPt is a weak spot. qwen3-14b scores 0.0%, #111 of 115, behind 96% of the models measured. The leader, GPT-5.6 Sol (max), reaches 32.3%. It covers unpublished research-level physics problems. The board median is 2.9%, so qwen3-14b is 2.9 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
Strengths and weaknesses
Reasoning, maths and science: uneven, ahead of 33% of the field on average across 3 benchmarks. Its best board here is OTIS Mock AIME (#87 of 189) and its weakest is CritPt (#111 of 115). General knowledge and chat: uneven, ahead of 46% of the field on average across 4 benchmarks. Its best board here is Epoch ECI (#60 of 204) and its weakest is LMCA (#76 of 108).
Price and value
The median input price across the 427 priced models is $0.500 per million tokens, so qwen3-14b costs less than the typical listing. Models that score within 1.5 points or better and cost less: Qwen2.5-Coder-7B-Instruct ($0.010, 49.5), DeepSeek V4.1 Flash (high) ($0.010, 48.9), Qwen2.5-Coder-3B-Instruct ($0.010, 46.5). Similar scores cost more on Qwen3-30B-A3B ($0.090) and DeepSeek-R1-Distill-Qwen-1.5B ($0.090).
Release history
The Alibaba model released before it was qwen-plus-2025-04-28 on 2025-04-28, which scores 48.9. The next one was Qwen3-30B-A3B on 2025-04-28, at 44.9.
Reading these numbers
The 9 scores on this page come from 1 source: Epoch AI Benchmarking Hub (9). No official leaderboard supplied any of them, so treat them as third-party measurements. The leaderboard also lists 1 other reasoning-effort setting of this model, which redirect here. This page shows the highest.
qwen3-14b is also listed at 1 other reasoning-effort setting; this page shows the highest-effort row, which is the one the leaderboard keeps by default.
Models ranked nearby
- #570 Llama 4 Maverick (FP8) (45.6)
- #571 mistral-large-2411 (45.6)
- #572 gemini-2.5-flash (45.6)
- #574 Yi-1.5-6B-Chat (45.6)
- #575 qwen3-coder-next (45.5)
- #576 Qwen1.5-14B-Chat (45.5)
More from Alibaba
- Qwen 3.6 Max (Preview) (#25, 54.1)
- Qwen3.8 Max (xhigh) (#37, 53.2)
- Qwen3.7 Max (#71, 52.3)
- ollama/qwen2.5-coder:32b (#93, 51.9)
- Qwen3 235B A22B diff, no think, Alibaba API (#122, 51.5)
- Qwen2.5-Coder-14B-Instruct (#135, 51.3)
Compare it on the live leaderboard → · How the aggregate score works