Qwen3 14B vs Llama 4 Maverick 17B Instruct vs DeepSeek-V3.1
Too close to call on our weighted score (Llama 4 Maverick 17B Instruct 58, DeepSeek-V3.1 55, Qwen3 14B 54). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3 14B
54/100- ECI138.2
- Price$0.35 / $1.40
- Context131K
MetaLlama 4 Maverick 17B Instruct
58/100- ECI132.2
- Price$0.321 / $0.91
- Context1M
DeepSeekDeepSeek-V3.1
55/100- ECI139.9
- Price$0.385 / $1.25
- Context131K
Too close to call
It is close. Our weighted score puts them within 2 points (Llama 4 Maverick 17B Instruct 58/100, DeepSeek-V3.1 55/100, Qwen3 14B 54/100), so choose by what matters most for your work: DeepSeek-V3.1 for raw capability, Llama 4 Maverick 17B Instruct on price and Llama 4 Maverick 17B Instruct for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek-V3.1Capabilities Index (ECI): DeepSeek-V3.1 139.9 · Qwen3 14B 138.2 · Llama 4 Maverick 17B Instruct 132.2
- Lowest priceLlama 4 Maverick 17B InstructLlama 4 Maverick 17B Instruct $0.468 · DeepSeek-V3.1 $0.601 · Qwen3 14B $0.613 per 1M tokens (3:1 blend)
- Longest contextLlama 4 Maverick 17B InstructLlama 4 Maverick 17B Instruct 1,000,000 · Qwen3 14B 131,072 · DeepSeek-V3.1 131,072 tokens
- Widest inputsLlama 4 Maverick 17B InstructQwen3 14B: Text · Llama 4 Maverick 17B Instruct: Text, Images · DeepSeek-V3.1: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3 14B | Llama 4 Maverick 17B Instruct | DeepSeek-V3.1 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 63 | 56 | 65 |
| Price | 25% | 60 | 66 | 60 |
| Inputs & features | 15% | 35 | 50 | 35 |
| Context window | 10% | 24 | 60 | 24 |
| Overall | 100% | 54/100 | 58/100 | 55/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3 14BAlibaba (Qwen) | Llama 4 Maverick 17B InstructMeta | DeepSeek-V3.1DeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 138.2 | 132.2 | 139.9 (best) |
| ECI rank | #107 of 148 | #122 of 148 | #100 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 63.8% | 67.0% (best) | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 66.4% (best) | 20.6% | — |
| Price per million tokens | |||
| Input | $0.35 | $0.321 (best) | $0.385 |
| Output | $1.40 | $0.91 (best) | $1.25 |
| Cached input | — | — | — |
| Blended (3:1) | $0.613 | $0.468 (best) | $0.601 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 6 providers | Median of 8 providers |
| Limits | |||
| Context window | 131,072 tokens | 1,000,000 tokens (best) | 131,072 tokens |
| Max output | 8,192 tokens | 16,384 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | OpenMIT License |
| API model ID | qwen3-14b | — | — |
| API providers | 1 | 6 | 8 (best) |
| Released | Apr 29, 2025 | Apr 5, 2025 | Aug 21, 2025 |
| Knowledge cutoff | Apr 2025 | Aug 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3 14B$6.30
- Llama 4 Maverick 17B Instruct$5.03
- DeepSeek-V3.1$6.35
Which should you choose?
Which is better: Qwen3 14B, Llama 4 Maverick 17B Instruct or DeepSeek-V3.1?
It is close. Our weighted score puts them within 2 points (Llama 4 Maverick 17B Instruct 58/100, DeepSeek-V3.1 55/100, Qwen3 14B 54/100), so choose by what matters most for your work: DeepSeek-V3.1 for raw capability, Llama 4 Maverick 17B Instruct on price and Llama 4 Maverick 17B Instruct for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3 14B, Llama 4 Maverick 17B Instruct or DeepSeek-V3.1?
Llama 4 Maverick 17B Instruct is cheaper at $0.321 input / $0.91 output per million tokens (median across 6 API providers). DeepSeek-V3.1 costs $0.385 input / $1.25 output per million tokens (median across 8 API providers); Qwen3 14B costs $0.35 input / $1.40 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.468 per million tokens for Llama 4 Maverick 17B Instruct versus $0.601 for DeepSeek-V3.1 (1.3× as much) and $0.613 for Qwen3 14B (1.3× as much).
Which scores higher on benchmarks?
DeepSeek-V3.1 scores higher on the Capabilities Index (ECI): DeepSeek-V3.1 139.9 (#100 of 148), Qwen3 14B 138.2 (#107 of 148) and Llama 4 Maverick 17B Instruct 132.2 (#122 of 148). The confidence ranges of the top two overlap (136.1–143.3 vs 133.5–140.1), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 14B, Llama 4 Maverick 17B Instruct and DeepSeek-V3.1 yet, so there is no like-for-like coding score. On overall capability, DeepSeek-V3.1 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Llama 4 Maverick 17B Instruct has the largest context window at 1,000,000 tokens, against 131,072 for Qwen3 14B and 131,072 for DeepSeek-V3.1. Maximum output per response: Qwen3 14B up to 8,192, Llama 4 Maverick 17B Instruct up to 16,384, DeepSeek-V3.1 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Qwen3 14B accepts text; Llama 4 Maverick 17B Instruct accepts text and images; DeepSeek-V3.1 accepts text. Llama 4 Maverick 17B Instruct handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (MIT License), so you can self-host them.
Which is newer?
DeepSeek-V3.1 is the newest, released Aug 21, 2025. Qwen3 14B came out Apr 29, 2025; Llama 4 Maverick 17B Instruct came out Apr 5, 2025. Knowledge cutoff: Qwen3 14B Apr 2025, Llama 4 Maverick 17B Instruct Aug 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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