Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. Pick Kimi K2.7 Code for long-horizon agentic software engineering or token-efficient reasoning (~30% fewer than k2.6). On a tight budget at scale, DeepSeek V4 is the value pick.
DeepSeek V4 (DeepSeek) and Kimi K2.7 Code (Moonshot AI) are two of the models people most often weigh against each other in 2026. DeepSeek V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Kimi K2.7 Code is moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: DeepSeek V4 is about 2.2× cheaper on input ($0.435/$0.87 per 1M tokens vs $0.95/$4 per 1M tokens) — meaningful once you are processing millions of tokens a month.
Context window: DeepSeek V4 holds 3.8× more — 1M (~1,500 pages) vs 256K (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Kimi K2.7 Code is the newer model by about 49 days (released June 12, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek V4
Kimi K2.7 Code
Provider
DeepSeek (China)
Moonshot AI (China)
Released
April 24, 2026
June 12, 2026
Context window
1M (~1,500 pages)
256K (~393 pages)
Price (in/out)
$0.435/$0.87 per 1M tokens
$0.95/$4 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, video, code
SWE-Bench Verified
80.6%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Near-frontier coding at ~1/12 the cost: DeepSeek V4 — A core design strength of DeepSeek V4.
Open MIT-licensed weights you can self-host: DeepSeek V4 — A core design strength of DeepSeek V4.
No long-context surcharge: DeepSeek V4 — A core design strength of DeepSeek V4.
Long-horizon agentic software engineering: Kimi K2.7 Code — A core design strength of Kimi K2.7 Code.
Token-efficient reasoning (~30% fewer than K2.6): Kimi K2.7 Code — A core design strength of Kimi K2.7 Code.
Open-weight 1T MoE, self-hostable: Kimi K2.7 Code — A core design strength of Kimi K2.7 Code.
Lowest cost at scale: DeepSeek V4 — At $0.435/$0.87 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: DeepSeek V4 — Its 1M window is about 3.8× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: DeepSeek V4 — At $0.435/$0.87 per 1M tokens it undercuts Kimi K2.7 Code, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: DeepSeek V4 — Larger 1M window fits more in one prompt.
Anyone whose priority is near-frontier coding at ~1/12 the cost: DeepSeek V4 — It is specifically built for that.
Anyone whose priority is long-horizon agentic software engineering: Kimi K2.7 Code — That is its strongest area.
DeepSeek V4: where it fits
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Released April 24, 2026 by DeepSeek, it is built for near-frontier coding at ~1/12 the cost, open MIT-licensed weights you can self-host, no long-context surcharge, and highest LiveCodeBench result.
Its trade-offs are real: trails the very best on hardest agentic coding, and text/code focused, less multimodal. At $0.435 in / $0.87 out per million tokens, it sits in the budget price band.
Kimi K2.7 Code: where it fits
Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. Released June 12, 2026 by Moonshot AI, it is built for long-horizon agentic software engineering, token-efficient reasoning (~30% fewer than K2.6), open-weight 1T MoE, self-hostable, and multi-turn tool use with preserved reasoning.
Its trade-offs: only self-reported benchmarks; no SWE-Bench Verified, and thinking mode and sampling params can't be disabled. At $0.95 in / $4 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
DeepSeek V4 and Kimi K2.7 Code overlap enough that the right pick depends on your specific job. DeepSeek V4 costs less per token; DeepSeek V4 holds the larger context; and each leads in its own area — DeepSeek V4 for near-frontier coding at ~1/12 the cost, Kimi K2.7 Code for long-horizon agentic software engineering. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is DeepSeek V4 or Kimi K2.7 Code better for coding?
Public SWE-Bench figures are not available for Kimi K2.7 Code, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V4 leans toward near-frontier coding at ~1/12 the cost while Kimi K2.7 Code leans toward long-horizon agentic software engineering, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4 or Kimi K2.7 Code?
DeepSeek V4 is cheaper — $0.435/$0.87 per 1M tokens vs $0.95/$4 per 1M tokens, roughly 2.2× apart on input.
Which has the bigger context window?
DeepSeek V4 — 1M vs 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4 and Kimi K2.7 Code together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4, Kimi K2.7 Code 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, DeepSeek V4 or Kimi K2.7 Code?
Kimi K2.7 Code — released June 12, 2026, about 49 days after DeepSeek V4.
DeepSeek V4 vs Kimi K2.7 Code
DeepSeek · China | Moonshot AI · China · Updated June 2026
Quick verdict
Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. Pick Kimi K2.7 Code for long-horizon agentic software engineering or token-efficient reasoning (~30% fewer than k2.6). On a tight budget at scale, DeepSeek V4 is the value pick.
DeepSeek V4 (DeepSeek) and Kimi K2.7 Code (Moonshot AI) are two of the models people most often weigh against each other in 2026. DeepSeek V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Kimi K2.7 Code is moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Price: DeepSeek V4 is about 2.2× cheaper on input ($0.435/$0.87 per 1M tokens vs $0.95/$4 per 1M tokens) — meaningful once you are processing millions of tokens a month.
▸Context window: DeepSeek V4 holds 3.8× more — 1M (~1,500 pages) vs 256K (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Kimi K2.7 Code is the newer model by about 49 days (released June 12, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek V4
Kimi K2.7 Code
Provider
DeepSeek (China)
Moonshot AI (China)
Released
April 24, 2026
June 12, 2026
Context window
1M (~1,500 pages)
256K (~393 pages)
Price (in/out)
$0.435/$0.87 per 1M tokens
$0.95/$4 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, video, code
SWE-Bench Verified
80.6%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Near-frontier coding at ~1/12 the cost
DeepSeek V4
A core design strength of DeepSeek V4.
Open MIT-licensed weights you can self-host
DeepSeek V4
A core design strength of DeepSeek V4.
No long-context surcharge
DeepSeek V4
A core design strength of DeepSeek V4.
Long-horizon agentic software engineering
Kimi K2.7 Code
A core design strength of Kimi K2.7 Code.
Token-efficient reasoning (~30% fewer than K2.6)
Kimi K2.7 Code
A core design strength of Kimi K2.7 Code.
Open-weight 1T MoE, self-hostable
Kimi K2.7 Code
A core design strength of Kimi K2.7 Code.
Lowest cost at scale
DeepSeek V4
At $0.435/$0.87 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
DeepSeek V4
Its 1M window is about 3.8× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ DeepSeek V4
At $0.435/$0.87 per 1M tokens it undercuts Kimi K2.7 Code, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ DeepSeek V4
Larger 1M window fits more in one prompt.
Anyone whose priority is near-frontier coding at ~1/12 the cost
→ DeepSeek V4
It is specifically built for that.
Anyone whose priority is long-horizon agentic software engineering
→ Kimi K2.7 Code
That is its strongest area.
DeepSeek V4: where it fits
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Released April 24, 2026 by DeepSeek, it is built for near-frontier coding at ~1/12 the cost, open MIT-licensed weights you can self-host, no long-context surcharge, and highest LiveCodeBench result.
Its trade-offs are real: trails the very best on hardest agentic coding, and text/code focused, less multimodal. At $0.435 in / $0.87 out per million tokens, it sits in the budget price band.
Kimi K2.7 Code: where it fits
Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. Released June 12, 2026 by Moonshot AI, it is built for long-horizon agentic software engineering, token-efficient reasoning (~30% fewer than K2.6), open-weight 1T MoE, self-hostable, and multi-turn tool use with preserved reasoning.
Its trade-offs: only self-reported benchmarks; no SWE-Bench Verified, and thinking mode and sampling params can't be disabled. At $0.95 in / $4 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
DeepSeek V4 and Kimi K2.7 Code overlap enough that the right pick depends on your specific job. DeepSeek V4 costs less per token; DeepSeek V4 holds the larger context; and each leads in its own area — DeepSeek V4 for near-frontier coding at ~1/12 the cost, Kimi K2.7 Code for long-horizon agentic software engineering. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both DeepSeek V4 and Kimi K2.7 Code 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.
Is DeepSeek V4 or Kimi K2.7 Code better for coding?
Public SWE-Bench figures are not available for Kimi K2.7 Code, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V4 leans toward near-frontier coding at ~1/12 the cost while Kimi K2.7 Code leans toward long-horizon agentic software engineering, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4 or Kimi K2.7 Code?
DeepSeek V4 is cheaper — $0.435/$0.87 per 1M tokens vs $0.95/$4 per 1M tokens, roughly 2.2× apart on input.
Which has the bigger context window?
DeepSeek V4 — 1M vs 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4 and Kimi K2.7 Code together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4, Kimi K2.7 Code 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, DeepSeek V4 or Kimi K2.7 Code?
Kimi K2.7 Code — released June 12, 2026, about 49 days after DeepSeek V4.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.