Command A vs DeepSeek V4 Flash 0731: which should you choose in 2026?
Command A (by Cohere) and DeepSeek V4 Flash 0731 (by DeepSeek) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.
There are not enough shared, protocol-compatible benchmark results to declare a performance leader.
DeepSeek V4 Flash 0731 is about 25.0× cheaper on a blended 3:1 input/output basis ($0.175 vs $4.38 per 1M tokens).
DeepSeek V4 Flash 0731 has the larger context window (1,310,720 tokens vs 256,000 tokens).
Choose Command A if…
- • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage
Choose DeepSeek V4 Flash 0731 if…
- • lower blended API cost matters for your workload
- • you work with longer documents, transcripts, or codebases
- • you need a model with explicit reasoning support
The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.
Performance benchmarks
Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.
| Benchmark | Command A | DeepSeek V4 Flash 0731 |
|---|---|---|
A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework. Guardrail-aware score | — | — |
A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing. Accuracy | — | — |
A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state. Mean task reward | — | — |
A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests. Resolved | — | — |
Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol. Mean task success | — | — |
A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench. Weighted peer-assessment score | — | — |
The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry. Accuracy | — | 90.8% Artificial Analysis |
A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged. Accuracy | — | 38.6% Artificial Analysis |
A contamination-resistant benchmark refreshed on a fixed release cadence. Scores from different LiveBench releases must never be compared as the same protocol. Mean of category averages | — | 74.2% LiveBench |
Benchmark sources
- GPQA Diamond Benchmark Leaderboard
- Humanity's Last Exam Benchmark Leaderboard
- LiveBench 2026-06-25 Leaderboard
Reviewed evidence last updated 2026-08-09.
Pricing, capabilities, and model facts
| Feature | Command A | DeepSeek V4 Flash 0731 |
|---|---|---|
| Context & model facts | ||
| Developer | Cohere | DeepSeek |
| API provider | Cohere | DeepSeek |
| Input context | 256,000 tokens | 1,310,720 tokens |
| Maximum output | 8,192 tokens | 131,072 tokens |
| Released | — | — |
| Added to Writingmate | Mar 13, 2025 | Jul 31, 2026 |
| License | Not available | Not available |
| Knowledge cutoff | 2024-08-31 | — |
| Capabilities | ||
| Inputs | Text | Text |
| Outputs | Text | Text |
| Provider endpoint accepts tool parameters | No | Yes |
| Reasoning | No | Yes |
| Vision | No | No |
| Image Generation | No | No |
| Video Generation | No | No |
| API pricing | ||
| Input (per 1M tokens) | $2.50 | $0.14 |
| Output (per 1M tokens) | $10.00 | $0.28 |
| Blended 3:1 input/output | $4.38 | $0.175 |
| API performance | ||
| p95 latency | Not measured | Not measured |
| Output throughput | Not measured | Not measured |
| Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table. | ||
Data sources
- Writingmate model catalog (pricing, limits, and availability)
Catalog data last updated Jul 31, 2026.
Why Pay for Multiple Subscriptions?
Comparing Command A from Cohere with DeepSeek V4 Flash 0731 from DeepSeek? Instead of managing separate API keys and subscriptions, get both with Writingmate.
| Plan | Price | Command A | DeepSeek V4 Flash 0731 | AI Images | AI Video |
|---|---|---|---|---|---|
Writingmate Pro Most popular | $20/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Writingmate Ultimate Power users | $60/mo | Included | Included | Nano Banana Pro, FLUX.2, DALL-E & more | Sora 2, VEO 3.1 |
Command A vs DeepSeek V4 Flash 0731 FAQ
Which is better, Command A or DeepSeek V4 Flash 0731?
There are not enough shared, protocol-compatible benchmark results to declare a performance leader. DeepSeek V4 Flash 0731 is about 25.0× cheaper on a blended 3:1 input/output basis ($0.175 vs $4.38 per 1M tokens). DeepSeek V4 Flash 0731 has the larger context window (1,310,720 tokens vs 256,000 tokens).
Which model is cheaper to use through an API?
DeepSeek V4 Flash 0731 is about 25.0× cheaper on a blended 3:1 input/output basis ($0.175 vs $4.38 per 1M tokens).
Which model supports more context?
DeepSeek V4 Flash 0731 has the larger context window (1,310,720 tokens vs 256,000 tokens).
Can I switch between Command A and DeepSeek V4 Flash 0731?
Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.
Related comparisons
More Command A comparisons
Muse Spark 1.2 Contributor
Meta
Muse Spark 1.2 contributor tier is a reasoning model from Meta designed for developers who want to start building at an even lower cost.
DeepSeek V4 Flash Vision Exp
DeepSeek
DeepSeek V4 Flash Vision Exp is a model by DeepSeek.
Ox Alpha
Stealth
Ox Alpha is a reasoning model designed for coding, sustained agentic work, and production workloads.
Hy-MT2-1.8B
Tencent
Hy-MT2-1.8B is a compact 1.8B-parameter translation model from Tencent.
Hy-MT2-30B-A3B
Tencent
Hy-MT2-30B-A3B is Tencent's flagship translation model in the Hy-MT2 family.
Hy-MT2-7B
Tencent
Hy-MT2-7B is a 7B-parameter translation model from Tencent.
More DeepSeek V4 Flash 0731 comparisons
Muse Spark 1.2 Contributor
Meta
Muse Spark 1.2 contributor tier is a reasoning model from Meta designed for developers who want to start building at an even lower cost.
Ox Alpha
Stealth
Ox Alpha is a reasoning model designed for coding, sustained agentic work, and production workloads.
GLM 5.3
Z.AI
GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks.
Qwen3.8 27B
Qwen
Qwen3.8 27B is an open-weight dense vision-language model from Qwen.
Gemini 3.7 Flash
Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning.
Seed 2.1 Turbo
ByteDance
Seed 2.1 Turbo is a multimodal model from ByteDance Seed for coding and long-horizon agent workflows.