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Comparison · 3 models · Updated Nov 2, 2026

DeepSeek R1 0528 Qwen3 8B vs Command R7B Arabic vs Baichuan M2 32B

Command R7B Arabic comes out ahead, 65 to 58 and 58 on our weighted score, and it is the cheaper option too.

  1. DeepSeek

    DeepSeek R1 0528 Qwen3 8B

    Released May 29, 2025

    58/100
    • ECI—
    • Price$0.06 / $0.09
    • Context131K
    Full specs Remove DeepSeek R1 0528 Qwen3 8B
  2. Our pick

    Cohere

    Command R7B Arabic

    Released Feb 27, 2025

    65/100
    • ECI—
    • Price$0.037 / $0.15
    • Context128K
    Full specs Remove Command R7B Arabic

  3. Baichuan

    Baichuan M2 32B

    Released Aug 13, 2025

    58/100
    • ECI—
    • Price$0.07 / $0.07
    • Context131K
    Full specs Remove Baichuan M2 32B
01 — Verdict

Command R7B Arabic is our pick

Command R7B Arabic is the better all-round choice, scoring 65/100 against DeepSeek R1 0528 Qwen3 8B (58) and Baichuan M2 32B (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 contextDeepSeek R1 0528 Qwen3 8B and Baichuan M2 32BDeepSeek R1 0528 Qwen3 8B 131,072 · Baichuan M2 32B 131,072 · Command R7B Arabic 128,000 tokens
  • Widest inputsSame inputsDeepSeek R1 0528 Qwen3 8B: Text · Command R7B Arabic: Text · Baichuan M2 32B: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightDeepSeek R1 0528 Qwen3 8BCommand R7B ArabicBaichuan M2 32B
Price50%100100100
Inputs & features30%103510
Context window20%252425
Overall100%58/10065/10058/100

Left out because at least one model lacks the data: capability. The remaining weights were rescaled.

02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

DeepSeek R1 0528 Qwen3 8B vs Command R7B Arabic vs Baichuan M2 32B specifications side by side
SpecificationDeepSeek R1 0528 Qwen3 8BDeepSeekCommand R7B ArabicCohereBaichuan M2 32BBaichuan
Capability
Capabilities Index (ECI)———
ECI rank———
GPQA DiamondGraduate-level science questions9.3%——
OTIS Mock AIME 2024–2025Competition mathematics43.9%——
Price per million tokens
Input$0.06$0.037 (best)$0.07
Output$0.09$0.15$0.07 (best)
Cached input———
Blended (3:1)$0.068$0.066 (best)$0.07
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 1 providersOfficial Cohere APIMedian of 1 providers
Limits
Context window131,072 tokens (best)128,000 tokens131,072 tokens (best)
Max output32,000 tokens4,000 tokens131,072 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesNoYes
Tool callingNoYesNo
Structured outputNoYesNo
Availability
WeightsOpenMITOpenOpenApache-2.0
API model ID—command-r7b-arabic-02-2025—
API providers111
ReleasedMay 29, 2025Feb 27, 2025Aug 13, 2025
Knowledge cutoff—Jun 1, 2024—
03 — Cost

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 0528 Qwen3 8B$0.78
  • Command R7B Arabic$0.675
  • Baichuan M2 32B$0.84
04 — Questions

Which should you choose?

Which is better: DeepSeek R1 0528 Qwen3 8B, Command R7B Arabic or Baichuan M2 32B?

Command R7B Arabic is the better all-round choice, scoring 65/100 against DeepSeek R1 0528 Qwen3 8B (58) and Baichuan M2 32B (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, DeepSeek R1 0528 Qwen3 8B, Command R7B Arabic or Baichuan M2 32B?

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. DeepSeek R1 0528 Qwen3 8B has not been scored yet, Command R7B Arabic has not been scored yet and Baichuan M2 32B has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for DeepSeek R1 0528 Qwen3 8B, Command R7B Arabic and Baichuan M2 32B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that DeepSeek R1 0528 Qwen3 8B and Baichuan M2 32B does not support tool calling, which most coding agents need.

Which has the bigger context window?

DeepSeek R1 0528 Qwen3 8B and Baichuan M2 32B have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Command R7B Arabic. Maximum output per response: DeepSeek R1 0528 Qwen3 8B up to 32,000, Command R7B Arabic up to 4,000, Baichuan M2 32B up to 131,072 tokens.

Which can read images, PDFs, audio or video?

DeepSeek R1 0528 Qwen3 8B accepts text; Command R7B Arabic accepts text; Baichuan M2 32B accepts text. They handle the same number of input types.

Are any of these open source?

Yes, all three publish their weights (MIT and Apache-2.0), 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.

05 — Keep comparing

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