Kimi K2.6 vs DeepSeek V4
The two best open-weight models in the world, head to head: Moonshot's Kimi K2.6 against DeepSeek V4. Both are Chinese, both publish weights, and both undercut US flagships by an order of magnitude.
Kimi K2.6 is the better agentic coder: 47.6% on CursorBench 3.1, the strongest open-weight result on the board, and it even has a free tier on OpenRouter to try before paying $0.67 per million input tokens.
DeepSeek V4 is the better all-round value: a 1M token context window (four times Kimi's 262K), solid reasoning (51.5 on the Intelligence Index in its best configuration), and a lower price at $0.435 per million input with notably cheap output at $0.87.
Pick Kimi K2.6 for agent work and coding; pick DeepSeek V4 for big-context work, summarization-heavy pipelines, and the lowest real cost. Both being open weights, you can also self-host either.
Prices and context are synced from live provider listings. Deep dives: Kimi K2.6 and DeepSeek V4.
Best published configuration per model. Every config and source is on the benchmark leaderboards.
Every published configuration for Kimi K2.6 and DeepSeek V4 on the benchmarks they share, charted side by side. Only these two models are plotted.
LiveCodeBench v6
Contamination-free competitive programming: problems are continuously collected from LeetCode, AtCoder and Codeforces after model cutoffs and scored as pass@1. Higher is better.
SWE-bench Verified
The most-cited agentic coding benchmark: can a model fix a real GitHub issue in a real repository? 500 human-validated tasks, scored by the repo's own tests. Higher is better.
DeepSWE
Datacurve's agentic coding benchmark: each model runs as an autonomous agent on real software engineering tasks and is scored on whether its final patch resolves the issue. Higher is better.
BrowseComp
OpenAI's hard web-browsing benchmark: 1,266 questions whose answers are hard to find but easy to verify, requiring persistent multi-step browsing. Higher is better.
Artificial Analysis Intelligence Index
The most-cited composite intelligence score: a 0–100 index combining knowledge, reasoning, math, coding, and agentic evaluations (GPQA Diamond, HLE, IFBench, SciCode, Terminal-Bench Hard, τ²-Bench, and more). Higher is better.
Is Kimi K2.6 better than DeepSeek V4?
For agentic coding, yes: Kimi K2.6's 47.6% on CursorBench 3.1 is the best open-weight result we track. For long-context work and overall value, DeepSeek V4 wins with a 1M token window and lower prices ($0.435 versus $0.67 per million input). They split the open-weight crown by use case.
Can I run Kimi K2.6 or DeepSeek V4 for free?
Kimi K2.6 has a genuine $0 tier on OpenRouter (rate limits apply), which is the easiest free path. DeepSeek V4 has no free hosted tier but its paid price is among the lowest anywhere. Both publish open weights, so self-hosting is the other free-at-the-margin option if you own the hardware.
Are these models good enough to replace Claude or GPT?
For most everyday agent work, yes: both handle tool calling and multi-step tasks reliably, and the price gap (5 to 20 times cheaper) buys a lot of retries. The closed flagships still lead on the hardest reasoning: CursorBench has frontier models in the 60s and 70s versus Kimi's 47.6%. The common pattern is a cheap open default with flagship escalation.
Type
Model comparisonKimi K2.6
Model pageDeepSeek V4
Model pageUpdated
June 2026