Command A vs Hunyuan Hy4
Cohere · Global | Tencent · China · Updated June 2026
Quick verdict
Pick Command A for enterprise rag and retrieval or strong long-context retrieval accuracy. Pick Hunyuan Hy4 for first hunyuan model with vision and computer use, adding image input to the text-only hy4 preview or tencent-reported gains over the preview of 19.1% on gamedev and 9.8% on gamelogic. Choose Command A if you need self-hosting or data privacy; Hunyuan Hy4 if you want a managed API.
Command A (Cohere) and Hunyuan Hy4 (Tencent) are two of the models people most often weigh against each other in 2026. Command A is cohere's enterprise-focused model built for retrieval-augmented and grounded workloads. Hunyuan Hy4 is tencent's November 4, 2026 full Hy4 release - a 1M-context model that adds vision and computer use at about $0.90/$2.70, with weights promised but not yet public. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: Hunyuan Hy4 is about 2.8× cheaper on input ($0.9/$2.7 per 1M tokens vs $2.5/$10 per 1M tokens) — meaningful once you are processing millions of tokens a month.
- ▸Context window: Hunyuan Hy4 holds 4.1× more — 1M tokens (~1,573 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: Hunyuan Hy4 is the newer model by about 20 months (released November 4, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Command A | Hunyuan Hy4 |
|---|---|---|
| Provider | Cohere (Global) | Tencent (China) |
| Released | March 2025 | November 4, 2026 |
| Context window | 256K (~384 pages) | 1M tokens (~1,573 pages) |
| Price (in/out) | $2.5/$10 per 1M tokens | $0.9/$2.7 per 1M tokens |
| Open weight? | Yes — self-hostable | No — API only |
| Modalities | text, code | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Enterprise RAG and retrieval
Command A
Cohere's enterprise-focused model built for retrieval-augmented and grounded workloads — and its weights are open while Hunyuan Hy4 is API-only.
Strong long-context retrieval accuracy
Command A
Command A lists strong long-context retrieval accuracy among its strengths; Hunyuan Hy4 does not.
Multilingual
Command A
Command A lists multilingual among its strengths; Hunyuan Hy4 does not.
First Hunyuan model with vision and computer use, adding image input to the text-only Hy4 Preview
Hunyuan Hy4
Tencent's November 4, 2026 full Hy4 release - a 1M-context model that adds vision and computer use at about $0.90/$2.70, with weights promised but not yet public — and it runs cheaper at $0.9/$2.7 per 1M tokens.
Tencent-reported gains over the preview of 19.1% on GameDev and 9.8% on GameLogic
Hunyuan Hy4
Tencent's November 4, 2026 full Hy4 release - a 1M-context model that adds vision and computer use at about $0.90/$2.70, with weights promised but not yet public — and it carries the larger 1M tokens context.
Tencent-reported financial evaluation of 3.04, ahead of DeepSeek V4.1 Flash 1017 at 2.97
Hunyuan Hy4
Tencent's November 4, 2026 full Hy4 release - a 1M-context model that adds vision and computer use at about $0.90/$2.70, with weights promised but not yet public — and it is the newer of the two.
Lowest cost at scale
Hunyuan Hy4
At $0.9/$2.7 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Hunyuan Hy4
Its 1M tokens window is about 4.1× larger than Command A's 256K, fitting roughly 1,573 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Hunyuan Hy4
At $0.9/$2.7 per 1M tokens it undercuts Command A, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Hunyuan Hy4
Larger 1M tokens window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ Command A
Open weights let you run it on your own hardware; Hunyuan Hy4 is API-only.
Anyone whose priority is enterprise rag and retrieval
→ Command A
It is specifically built for that.
Anyone whose priority is first hunyuan model with vision and computer use, adding image input to the text-only hy4 preview
→ Hunyuan Hy4
That is its strongest area.
Command A: where it fits
Cohere's enterprise-focused model built for retrieval-augmented and grounded workloads. Released March 2025 by Cohere, it is built for enterprise RAG and retrieval, strong long-context retrieval accuracy, multilingual, and tool use.
Its trade-offs are real: less consumer presence, and narrower modality support. At $2.5 in / $10 out per million tokens, it sits in the mid price band.
Hunyuan Hy4: where it fits
Tencent's November 4, 2026 full Hy4 release - a 1M-context model that adds vision and computer use at about $0.90/$2.70, with weights promised but not yet public. Released November 4, 2026 by Tencent, it is built for first Hunyuan model with vision and computer use, adding image input to the text-only Hy4 Preview, tencent-reported gains over the preview of 19.1% on GameDev and 9.8% on GameLogic, tencent-reported financial evaluation of 3.04, ahead of DeepSeek V4.1 Flash 1017 at 2.97, and about $0.90/$2.70 per million tokens, with 50% off on Tencent Cloud TokenHub through December 31, 2026 (OpenRouter lists $0.417/$1.25 with that discount).
Its trade-offs: weights not public as of November 5, 2026 - Tencent says weights and system cards will come in the coming days, and no license is announced yet, only the earlier Hy4 Preview is downloadable today (Apache 2.0), published improvements are Tencent's own and mostly relative to the preview, with no SWE-bench Verified figure, and sources differ on list price: Tencent says about $0.90/$2.70, while OpenRouter and Vals show $0.83/$2.50. At $0.9 in / $2.7 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
The defining split here is open vs. closed. Command A gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Hunyuan Hy4 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both Command A and Hunyuan Hy4 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
See pricingFrequently asked questions
Is Command A or Hunyuan Hy4 better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Command A leans toward enterprise rag and retrieval while Hunyuan Hy4 leans toward first hunyuan model with vision and computer use, adding image input to the text-only hy4 preview, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Command A or Hunyuan Hy4?
Command A is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Hunyuan Hy4 is API-metered at $0.9/$2.7 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Hunyuan Hy4 — 1M tokens vs 256K, about 4.1× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Command A and Hunyuan Hy4 together?
Yes — a multi-model platform like LumiChats gives you Command A, Hunyuan Hy4 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Command A or Hunyuan Hy4?
Hunyuan Hy4 — released November 4, 2026, about 20 months after Command A.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.