Baichuan M2 32B vs Command R7B Arabic vs DeepSeek R1 0528 Qwen3 8B
Command R7B Arabic comes out ahead, 65 to 58 and 58 on our weighted score, and it is the cheaper option too.
BaichuanBaichuan M2 32B
58/100- ECI—
- Price$0.07 / $0.07
- Context131K
- Our pick
CohereCommand R7B Arabic
65/100- ECI—
- Price$0.037 / $0.15
- Context128K
DeepSeekDeepSeek R1 0528 Qwen3 8B
58/100- ECI—
- Price$0.06 / $0.09
- Context131K
Command R7B Arabic is our pick
Command R7B Arabic is the better all-round choice, scoring 65/100 against Baichuan M2 32B (58) and DeepSeek R1 0528 Qwen3 8B (58). 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 R7B ArabicCommand R7B Arabic $0.066 · DeepSeek R1 0528 Qwen3 8B $0.068 · Baichuan M2 32B $0.07 per 1M tokens (3:1 blend)
- Longest contextBaichuan M2 32B and DeepSeek R1 0528 Qwen3 8BBaichuan M2 32B 131,072 · DeepSeek R1 0528 Qwen3 8B 131,072 · Command R7B Arabic 128,000 tokens
- Widest inputsSame inputsBaichuan M2 32B: Text · Command R7B Arabic: Text · DeepSeek R1 0528 Qwen3 8B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Baichuan M2 32B | Command R7B Arabic | DeepSeek R1 0528 Qwen3 8B |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 100 |
| Inputs & features | 30% | 10 | 35 | 10 |
| Context window | 20% | 25 | 24 | 25 |
| Overall | 100% | 58/100 | 65/100 | 58/100 |
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 | Baichuan M2 32BBaichuan | Command R7B ArabicCohere | DeepSeek R1 0528 Qwen3 8BDeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| GPQA DiamondGraduate-level science questions | — | — | 9.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 43.9% |
| Price per million tokens | |||
| Input | $0.07 | $0.037 (best) | $0.06 |
| Output | $0.07 (best) | $0.15 | $0.09 |
| Cached input | — | — | — |
| Blended (3:1) | $0.07 | $0.066 (best) | $0.068 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Cohere API | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Max output | 131,072 tokens (best) | 4,000 tokens | 32,000 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 | No | Yes | No |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | OpenApache-2.0 | Open | OpenMIT |
| API model ID | — | command-r7b-arabic-02-2025 | — |
| API providers | 1 | 1 | 1 |
| Released | Aug 13, 2025 | Feb 27, 2025 | May 29, 2025 |
| Knowledge cutoff | — | Jun 1, 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Baichuan M2 32B$0.84
- Command R7B Arabic$0.675
- DeepSeek R1 0528 Qwen3 8B$0.78
Which should you choose?
Which is better: Baichuan M2 32B, Command R7B Arabic or DeepSeek R1 0528 Qwen3 8B?
Command R7B Arabic is the better all-round choice, scoring 65/100 against Baichuan M2 32B (58) and DeepSeek R1 0528 Qwen3 8B (58). 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, Baichuan M2 32B, Command R7B Arabic or DeepSeek R1 0528 Qwen3 8B?
Command R7B Arabic is cheaper at $0.037 input / $0.15 output per million tokens (official Cohere API price). DeepSeek R1 0528 Qwen3 8B costs $0.06 input / $0.09 output per million tokens (median across 1 API provider); Baichuan M2 32B costs $0.07 input / $0.07 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 Arabic versus $0.068 for DeepSeek R1 0528 Qwen3 8B (1× as much) and $0.07 for Baichuan M2 32B (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Baichuan M2 32B has not been scored yet, Command R7B Arabic has not been scored yet and DeepSeek R1 0528 Qwen3 8B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Baichuan M2 32B, Command R7B Arabic and DeepSeek R1 0528 Qwen3 8B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Baichuan M2 32B and DeepSeek R1 0528 Qwen3 8B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Baichuan M2 32B and DeepSeek R1 0528 Qwen3 8B have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Command R7B Arabic. Maximum output per response: Baichuan M2 32B up to 131,072, Command R7B Arabic up to 4,000, DeepSeek R1 0528 Qwen3 8B up to 32,000 tokens.
Which can read images, PDFs, audio or video?
Baichuan M2 32B accepts text; Command R7B Arabic accepts text; DeepSeek R1 0528 Qwen3 8B accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Apache-2.0 and MIT), so you can self-host them.
Which is newer?
Baichuan M2 32B is the newest, released Aug 13, 2025. DeepSeek R1 0528 Qwen3 8B came out May 29, 2025; Command R7B Arabic came out Feb 27, 2025. Knowledge cutoff: Command R7B Arabic Jun 1, 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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