DeepSeek-R1 vs Llama-3.2-3B vs Qwen3 235B-A22B
Llama-3.2-3B comes out ahead, 49 to 39 and 38 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
Alibaba (Qwen)Qwen3 235B-A22B
38/100- ECI139.4
- Price$0.70 / $2.80
- Context131K
Llama-3.2-3B is our pick
Llama-3.2-3B is the better all-round choice, scoring 49/100 against DeepSeek-R1 (39) and Qwen3 235B-A22B (38). It leads on price. 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 · Qwen3 235B-A22B $1.23 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-3B and Qwen3 235B-A22BLlama-3.2-3B 131,072 · Qwen3 235B-A22B 131,072 · DeepSeek-R1 128,000 tokens
- Widest inputsSame inputsDeepSeek-R1: Text · Llama-3.2-3B: Text · Qwen3 235B-A22B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek-R1 | Llama-3.2-3B | Qwen3 235B-A22B |
|---|---|---|---|---|
| Price | 50% | 47 | 88 | 46 |
| Inputs & features | 30% | 35 | 0 | 35 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 39/100 | 49/100 | 38/100 |
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 | Qwen3 235B-A22BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 139.0 | — | 139.4 (best) |
| ECI rank | #104 of 148 | — | #103 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 71.7% (best) | — | 70.7% |
| OTIS Mock AIME 2024–2025Competition mathematics | 53.3% | — | — |
| Price per million tokens | |||
| Input | $0.70 | $0.10 (best) | $0.70 |
| Output | $2.60 | $0.335 (best) | $2.80 |
| Cached input | — | — | — |
| Blended (3:1) | $1.18 | $0.159 (best) | $1.23 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 11 providers | Median of 3 providers | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Max output | 32,768 tokens (best) | 8,192 tokens | 16,384 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenLlama 3.2 Community License | Open |
| API model ID | — | — | qwen3-235b-a22b |
| API providers | 12 (best) | 3 | 7 |
| Released | Jan 20, 2025 | Sep 25, 2024 | Apr 28, 2025 |
| Knowledge cutoff | Jul 2024 | Dec 2023 | Apr 2025 |
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
- Qwen3 235B-A22B$12.60
Which should you choose?
Which is better: DeepSeek-R1, Llama-3.2-3B or Qwen3 235B-A22B?
Llama-3.2-3B is the better all-round choice, scoring 49/100 against DeepSeek-R1 (39) and Qwen3 235B-A22B (38). It leads on price. 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, Llama-3.2-3B or Qwen3 235B-A22B?
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); Qwen3 235B-A22B costs $0.70 input / $2.80 output per million tokens (official Alibaba API price). 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) and $1.23 for Qwen3 235B-A22B (7.7× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. DeepSeek-R1 has an ECI of 139.0, Llama-3.2-3B has not been scored yet and Qwen3 235B-A22B has an ECI of 139.4.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-R1, Llama-3.2-3B and Qwen3 235B-A22B 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 and Qwen3 235B-A22B have the largest context windows (131,072 and 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, Qwen3 235B-A22B up to 16,384 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-R1 accepts text; Llama-3.2-3B accepts text; Qwen3 235B-A22B accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Llama 3.2 Community License), so you can self-host them.
Which is newer?
Qwen3 235B-A22B is the newest, released Apr 28, 2025. DeepSeek-R1 came out Jan 20, 2025; Llama-3.2-3B came out Sep 25, 2024. Knowledge cutoff: DeepSeek-R1 Jul 2024, Llama-3.2-3B Dec 2023, Qwen3 235B-A22B Apr 2025.
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.
