The frontier open-weight matchup of 2026. K2.5 is multimodal (text, image, video) with Agent Swarm orchestration and a lower headline input price; V4 Pro is text-only but takes the context crown at 1M tokens with markedly cheaper long-context inference. Vision-driven agents choose Kimi; document- and code-scale reasoning chooses DeepSeek.
DeepSeek V4 vs Kimi K2.5 at a glance
| DeepSeek V4 | Kimi K2.5 | |
|---|---|---|
| Vendor | DeepSeek (China) | Moonshot AI (China) |
| Open weights | Yes — downloadable | Yes — downloadable |
| License | MIT — weights free to download, self-host, fine-tune, and use commercially | Modified MIT — free commercial use; attribution required above 100M monthly active users or $20M monthly revenue |
| Parameters | 1.6T (49B active) | 1T (32B active) |
| Context window | 1M tokens | 256K tokens |
| Modalities | text | text, image, video |
| Pricing | $1.74 in / $3.48 out per 1M tokens | $0.60 in / $2.50 out per 1M tokens |
| Released | 2026-04-24 | 2026-01-27 |
Specs and pricing verified July 2026.
About DeepSeek V4
DeepSeek V4 Pro is the flagship of DeepSeek's V4 family, released on April 24, 2026, and the strongest argument yet that open-weight models compete at the frontier. It is a 1.6-trillion-parameter mixture-of-experts model that activates just 49B parameters per token, pairs a 1M-token context window with up to 384K tokens of output, and ships under a plain MIT license — no usage thresholds, no acceptable-use gate.
The headline engineering story is the attention stack. V4 combines Compressed Sparse Attention (CSA), which compresses KV entries 4× along the sequence with softmax-gated pooling, with Heavily Compressed Attention (HCA) at 128× compression. The practical result: at 1M-token context, V4 Pro needs roughly 27% of the single-token inference FLOPs and 10% of the KV cache of DeepSeek-V3.2. Long context stopped being a luxury and became the default across DeepSeek's services.
Full specs, benchmarks, and hardware guidance: the DeepSeek V4 page.
About Kimi K2.5
Kimi K2.5 is Moonshot AI's trillion-parameter multimodal agent model, released January 27, 2026. Built by continual-pretraining on ~15 trillion mixed visual and text tokens on top of the Kimi-K2 base, it natively understands text, images, and video, activates 32B parameters per token, and ships both instant and thinking modes.
Its defining feature is agentic: K2.5 introduced Agent Swarm, coordinating up to 100 specialized agents on a single task, and posted 76.8% on SWE-bench Verified — frontier-class coding from an open-weight release. The Modified MIT license is effectively free for everyone below 100M monthly users or $20M monthly revenue, at which point attribution (not payment) kicks in.
Full specs, benchmarks, and hardware guidance: the Kimi K2.5 page.
Choosing between them
Choose DeepSeek V4 for:
- Frontier-level reasoning and coding on an open license
- Very long documents and repositories (1M-token context as the default)
- Teams that need Anthropic/OpenAI API compatibility with open-weight economics
Choose Kimi K2.5 for:
- Multimodal agents that need vision and video, not just text
- Frontier coding (76.8% SWE-bench Verified) on open weights
- Multi-agent orchestration workloads (Agent Swarm)
Frequently asked questions
Is DeepSeek V4 better than Kimi K2.5?
The frontier open-weight matchup of 2026. K2.5 is multimodal (text, image, video) with Agent Swarm orchestration and a lower headline input price; V4 Pro is text-only but takes the context crown at 1M tokens with markedly cheaper long-context inference. Vision-driven agents choose Kimi; document- and code-scale reasoning chooses DeepSeek.
Which is cheaper: DeepSeek V4 or Kimi K2.5?
DeepSeek V4: $1.74 in / $3.48 out per 1M tokens. Kimi K2.5: $0.60 in / $2.50 out per 1M tokens. Both are open-weight, so self-hosting costs depend on your hardware and utilization.
Can I self-host DeepSeek V4 and Kimi K2.5?
DeepSeek V4: yes — weights are published under MIT. Kimi K2.5: yes — weights are published under Modified MIT.