Command R7B vs Llama-3.2-1B vs Qwen2.5-Coder-0.5B
Command R7B comes out ahead, 62 to 55 and 49 on our weighted score, and it is the cheaper option too.
- Our pick
CohereCommand R7B
62/100- ECI—
- Price$0.037 / $0.15
- Context128K
MetaLlama-3.2-1B
55/100- ECI102.0
- Price$0.064 / $0.15
- Context131K
Alibaba (Qwen)Qwen2.5-Coder-0.5B
49/100- ECI88.2
- Price$0.10 / $0.10
- Context33K
Command R7B is our pick
Command R7B is the better all-round choice, scoring 62/100 against Llama-3.2-1B (55) and Qwen2.5-Coder-0.5B (49). It leads 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 priceCommand R7BCommand R7B $0.066 · Llama-3.2-1B $0.085 · Qwen2.5-Coder-0.5B $0.10 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-1BLlama-3.2-1B 131,072 · Command R7B 128,000 · Qwen2.5-Coder-0.5B 32,768 tokens
- Widest inputsSame inputsCommand R7B: Text · Llama-3.2-1B: Text · Qwen2.5-Coder-0.5B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Command R7B | Llama-3.2-1B | Qwen2.5-Coder-0.5B |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 97 |
| Inputs & features | 30% | 25 | 0 | 0 |
| Context window | 20% | 24 | 24 | 0 |
| Overall | 100% | 62/100 | 55/100 | 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 | Command R7BCohere | Llama-3.2-1BMeta | Qwen2.5-Coder-0.5BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 102.0 (best) | 88.2 |
| ECI rank | — | #147 of 148 (best) | #148 of 148 |
| GPQA DiamondGraduate-level science questions | — | 23.9% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 0.6% | — |
| Price per million tokens | |||
| Input | $0.037 (best) | $0.064 | $0.10 |
| Output | $0.15 | $0.15 | $0.10 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.066 (best) | $0.085 | $0.10 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Median of 2 providers | Median of 1 providers |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 32,768 tokens |
| Max output | 4,000 tokens | 8,192 tokens (best) | 8,192 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | No | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenLlama 3.2 Community License | OpenApache 2.0 |
| API model ID | command-r7b-12-2024 | — | — |
| API providers | 5 (best) | 2 | 1 |
| Released | Dec 2, 2024 | Sep 25, 2024 | Nov 12, 2024 |
| Knowledge cutoff | Jun 1, 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.
- Command R7B$0.675
- Llama-3.2-1B$0.936
- Qwen2.5-Coder-0.5B$1.20
Which should you choose?
Which is better: Command R7B, Llama-3.2-1B or Qwen2.5-Coder-0.5B?
Command R7B is the better all-round choice, scoring 62/100 against Llama-3.2-1B (55) and Qwen2.5-Coder-0.5B (49). It leads 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, Command R7B, Llama-3.2-1B or Qwen2.5-Coder-0.5B?
Command R7B is cheaper at $0.037 input / $0.15 output per million tokens (official Cohere API price). Llama-3.2-1B costs $0.064 input / $0.15 output per million tokens (median across 2 API providers); Qwen2.5-Coder-0.5B costs $0.10 input / $0.10 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.066 per million tokens for Command R7B versus $0.085 for Llama-3.2-1B (1.3× as much) and $0.10 for Qwen2.5-Coder-0.5B (1.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Command R7B has not been scored yet, Llama-3.2-1B has an ECI of 102.0 and Qwen2.5-Coder-0.5B has an ECI of 88.2.
Which is better for coding?
There are no published SWE-bench Verified results for Command R7B, Llama-3.2-1B and Qwen2.5-Coder-0.5B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Llama-3.2-1B and Qwen2.5-Coder-0.5B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Llama-3.2-1B has the largest context window at 131,072 tokens, against 128,000 for Command R7B and 32,768 for Qwen2.5-Coder-0.5B. Maximum output per response: Command R7B up to 4,000, Llama-3.2-1B up to 8,192, Qwen2.5-Coder-0.5B up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Command R7B accepts text; Llama-3.2-1B accepts text; Qwen2.5-Coder-0.5B 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 and Apache 2.0), so you can self-host them.
Which is newer?
Command R7B is the newest, released Dec 2, 2024. Qwen2.5-Coder-0.5B came out Nov 12, 2024; Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Command R7B Jun 1, 2024, Llama-3.2-1B 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.
