DeepSeek-R1 vs Llama-3.2-3B
Llama-3.2-3B comes out ahead, 49 to 39 on our weighted score, and it is the cheaper option too.
DeepSeekDeepSeek-R1
39/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
- Our pick
MetaLlama-3.2-3B
49/100- ECI—
- Price$0.10 / $0.335
- Context131K
Add a model
Make it a three-way comparison.
Llama-3.2-3B is our pick
Llama-3.2-3B is the better all-round choice, scoring 49/100 against DeepSeek-R1 (39). It leads on price. DeepSeek-R1 wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceLlama-3.2-3BLlama-3.2-3B $0.159 · DeepSeek-R1 $1.18 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-3BLlama-3.2-3B 131,072 · DeepSeek-R1 128,000 tokens
- Widest inputsSame inputsDeepSeek-R1: Text · Llama-3.2-3B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek-R1 | Llama-3.2-3B |
|---|---|---|---|
| Price | 50% | 47 | 88 |
| Inputs & features | 30% | 35 | 0 |
| Context window | 20% | 24 | 24 |
| Overall | 100% | 39/100 | 49/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek-R1DeepSeek | Llama-3.2-3BMeta |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 139.0 | — |
| ECI rank | #104 of 148 | — |
| GPQA DiamondGraduate-level science questions | 71.7% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 53.3% | — |
| Price per million tokens | ||
| Input | $0.70 | $0.10 (best) |
| Output | $2.60 | $0.335 (best) |
| Cached input | — | — |
| Blended (3:1) | $1.18 | $0.159 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 11 providers | Median of 3 providers |
| Limits | ||
| Context window | 128,000 tokens | 131,072 tokens (best) |
| Max output | 32,768 tokens (best) | 8,192 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | No |
| Tool calling | Yes | No |
| Structured output | No | No |
| Availability | ||
| Weights | Open | OpenLlama 3.2 Community License |
| API model ID | — | — |
| API providers | 12 (best) | 3 |
| Released | Jan 20, 2025 | Sep 25, 2024 |
| Knowledge cutoff | Jul 2024 | Dec 2023 |
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$12.20
- Llama-3.2-3B$1.67
Which should you choose?
Which is better: DeepSeek-R1 or Llama-3.2-3B?
Llama-3.2-3B is the better all-round choice, scoring 49/100 against DeepSeek-R1 (39). It leads on price. DeepSeek-R1 wins on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, DeepSeek-R1 or Llama-3.2-3B?
Llama-3.2-3B is cheaper at $0.10 input / $0.335 output per million tokens (median across 3 API providers). DeepSeek-R1 costs $0.70 input / $2.60 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.159 per million tokens for Llama-3.2-3B versus $1.18 for DeepSeek-R1 (7.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. DeepSeek-R1 has an ECI of 139.0 and Llama-3.2-3B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-R1 and Llama-3.2-3B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Llama-3.2-3B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Llama-3.2-3B has the largest context window at 131,072 tokens, against 128,000 for DeepSeek-R1. Maximum output per response: DeepSeek-R1 up to 32,768, Llama-3.2-3B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-R1 accepts text; Llama-3.2-3B accepts text. They handle the same number of input types.
Are any of these open source?
Yes, both publish their weights (Llama 3.2 Community License), so you can self-host them.
Which is newer?
DeepSeek-R1 is the newest, released Jan 20, 2025. Llama-3.2-3B came out Sep 25, 2024. Knowledge cutoff: DeepSeek-R1 Jul 2024, Llama-3.2-3B Dec 2023.
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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