The two strongest open-weight families, split by modality and context. Kimi K2.5 brings native image and video understanding plus Agent Swarm multi-agent orchestration; DeepSeek V4 brings the 1M-token context window, cleaner plain-MIT licensing, and cheaper long-context inference. Multimodal agents point to Kimi; massive-document and repository-scale work points to DeepSeek.
Kimi K2 vs DeepSeek at a glance
| Kimi K2.5 | DeepSeek V4 | |
|---|---|---|
| Vendor | Moonshot AI (China) | DeepSeek (China) |
| Open weights | Yes — downloadable | Yes — downloadable |
| License | Modified MIT — free commercial use; attribution required above 100M monthly active users or $20M monthly revenue | MIT — weights free to download, self-host, fine-tune, and use commercially |
| Parameters | 1T (32B active) | 1.6T (49B active) |
| Context window | 256K tokens | 1M tokens |
| Modalities | text, image, video | text |
| Pricing | $0.60 in / $2.50 out per 1M tokens | $1.74 in / $3.48 out per 1M tokens |
| Released | 2026-01-27 | 2026-04-24 |
Specs and pricing verified July 2026.
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.
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.
Choosing between them
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)
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
Frequently asked questions
Is Kimi K2.5 better than DeepSeek V4?
The two strongest open-weight families, split by modality and context. Kimi K2.5 brings native image and video understanding plus Agent Swarm multi-agent orchestration; DeepSeek V4 brings the 1M-token context window, cleaner plain-MIT licensing, and cheaper long-context inference. Multimodal agents point to Kimi; massive-document and repository-scale work points to DeepSeek.
Which is cheaper: Kimi K2.5 or DeepSeek V4?
Kimi K2.5: $0.60 in / $2.50 out per 1M tokens. DeepSeek V4: $1.74 in / $3.48 out per 1M tokens. Both are open-weight, so self-hosting costs depend on your hardware and utilization.
Can I self-host Kimi K2.5 and DeepSeek V4?
Kimi K2.5: yes — weights are published under Modified MIT. DeepSeek V4: yes — weights are published under MIT.