DeepSeek-R1-Distill-Qwen-32B vs Llama 4 Maverick 17B Instruct
Too close to call on our weighted score (Llama 4 Maverick 17B Instruct 47, DeepSeek-R1-Distill-Qwen-32B 45). The right pick depends on what you value most.
DeepSeekDeepSeek-R1-Distill-Qwen-32B
45/100- ECI—
- Price—
- Context131K
MetaLlama 4 Maverick 17B Instruct
47/100- ECI132.3
- Price$0.245 / $0.838
- Context1M
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Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within 2 points (Llama 4 Maverick 17B Instruct 47/100, DeepSeek-R1-Distill-Qwen-32B 45/100), so choose by what matters most for your work: DeepSeek-R1-Distill-Qwen-32B for raw capability, Llama 4 Maverick 17B Instruct on price and Llama 4 Maverick 17B Instruct for long inputs. The score weighs capability 67%, inputs & features 20%, context window 13%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because DeepSeek-R1-Distill-Qwen-32B has no Capabilities Index score yet.
- CapabilityDeepSeek-R1-Distill-Qwen-32BShared benchmarks: DeepSeek-R1-Distill-Qwen-32B 59.9% · Llama 4 Maverick 17B Instruct 43.8%
- Lowest priceLlama 4 Maverick 17B InstructLlama 4 Maverick 17B Instruct $0.393 per 1M tokens (3:1 blend) · DeepSeek-R1-Distill-Qwen-32B unpriced
- Longest contextLlama 4 Maverick 17B InstructLlama 4 Maverick 17B Instruct 1,000,000 · DeepSeek-R1-Distill-Qwen-32B 131,072 tokens
- Widest inputsLlama 4 Maverick 17B InstructDeepSeek-R1-Distill-Qwen-32B: Text · Llama 4 Maverick 17B Instruct: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek-R1-Distill-Qwen-32B | Llama 4 Maverick 17B Instruct |
|---|---|---|---|
| CapabilityShared benchmarks | 67% | 60 | 44 |
| Inputs & features | 20% | 10 | 50 |
| Context window | 13% | 25 | 60 |
| Overall | 100% | 45/100 | 47/100 |
Left out because at least one model lacks the data: price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek-R1-Distill-Qwen-32BDeepSeek | Llama 4 Maverick 17B InstructMeta |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | 132.3 |
| ECI rank | — | #135 of 165 |
| GPQA DiamondGraduate-level science questions | 64.1% | 67.0% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 55.6% (best) | 20.6% |
| Price per million tokens | ||
| Input | — | $0.245 |
| Output | — | $0.838 |
| Cached input | — | — |
| Blended (3:1) | — | $0.393 |
| Long-context rate | — | Same rate |
| Price source | — | Median of 16 providers |
| Limits | ||
| Context window | 131,072 tokens | 1,000,000 tokens (best) |
| Max output | 32,768 tokens (best) | 16,384 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | No |
| Tool calling | No | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Open |
| API model ID | — | — |
| API providers | — | 16 |
| Released | Jan 20, 2025 | Apr 5, 2025 |
| Knowledge cutoff | — | 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.
- DeepSeek-R1-Distill-Qwen-32B—
- Llama 4 Maverick 17B Instruct$4.13
Which should you choose?
Which is better: DeepSeek-R1-Distill-Qwen-32B or Llama 4 Maverick 17B Instruct?
It is close. Our weighted score puts them within 2 points (Llama 4 Maverick 17B Instruct 47/100, DeepSeek-R1-Distill-Qwen-32B 45/100), so choose by what matters most for your work: DeepSeek-R1-Distill-Qwen-32B for raw capability, Llama 4 Maverick 17B Instruct on price and Llama 4 Maverick 17B Instruct for long inputs. The score weighs capability 67%, inputs & features 20%, context window 13%. Capability uses the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025), because DeepSeek-R1-Distill-Qwen-32B has no Capabilities Index score yet.
Which is cheaper, DeepSeek-R1-Distill-Qwen-32B or Llama 4 Maverick 17B Instruct?
Llama 4 Maverick 17B Instruct is cheaper at $0.245 input / $0.838 output per million tokens (median across 16 API providers). . At a typical mix of three input tokens to one output token, that is $0.393 per million tokens for Llama 4 Maverick 17B Instruct versus . DeepSeek-R1-Distill-Qwen-32B has no published per-token price.
Which scores higher on benchmarks?
Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond and OTIS Mock AIME 2024–2025): DeepSeek-R1-Distill-Qwen-32B 59.9% and Llama 4 Maverick 17B Instruct 43.8%. On individual benchmarks: GPQA Diamond — Llama 4 Maverick 17B Instruct 67.0%, DeepSeek-R1-Distill-Qwen-32B 64.1%; OTIS Mock AIME 2024–2025 — DeepSeek-R1-Distill-Qwen-32B 55.6%, Llama 4 Maverick 17B Instruct 20.6%.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-R1-Distill-Qwen-32B and Llama 4 Maverick 17B Instruct yet, so there is no like-for-like coding score. On overall capability, DeepSeek-R1-Distill-Qwen-32B leads, which tends to carry over to coding, but test on your own codebase. Note that DeepSeek-R1-Distill-Qwen-32B does not support tool calling, which most coding agents need.
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 DeepSeek-R1-Distill-Qwen-32B. Maximum output per response: DeepSeek-R1-Distill-Qwen-32B up to 32,768, Llama 4 Maverick 17B Instruct up to 16,384 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-R1-Distill-Qwen-32B accepts text; Llama 4 Maverick 17B Instruct accepts text and images. Llama 4 Maverick 17B Instruct handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
Llama 4 Maverick 17B Instruct is the newest, released Apr 5, 2025. DeepSeek-R1-Distill-Qwen-32B came out Jan 20, 2025. Knowledge cutoff: 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 Nov 2, 2026.
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