Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. 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 R1 is the value pick.
DeepSeek R1 (DeepSeek) and Kimi K2.7 Code (Moonshot AI) are two of the models people most often weigh against each other in 2026. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. 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 R1 is about 1.7× cheaper on input ($0.55/$2.19 per 1M tokens vs $0.95/$4 per 1M tokens) — modest, but it adds up at steady volume.
Context window: Kimi K2.7 Code holds 2× more — 256K (~393 pages) vs 128K (~192 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 17 months (released June 12, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek R1
Kimi K2.7 Code
Provider
DeepSeek (China)
Moonshot AI (China)
Released
January 2025
June 12, 2026
Context window
128K (~192 pages)
256K (~393 pages)
Price (in/out)
$0.55/$2.19 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
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open-weight reasoning model: DeepSeek R1 — A core design strength of DeepSeek R1.
Transparent chain-of-thought: DeepSeek R1 — A core design strength of DeepSeek R1.
Low cost: DeepSeek R1 — A core design strength of DeepSeek R1.
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 R1 — At $0.55/$2.19 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Kimi K2.7 Code — Its 256K window is about 2× larger, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: DeepSeek R1 — At $0.55/$2.19 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: Kimi K2.7 Code — Larger 256K window fits more in one prompt.
Anyone whose priority is open-weight reasoning model: DeepSeek R1 — 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 R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs are real: older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 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 R1 and Kimi K2.7 Code overlap enough that the right pick depends on your specific job. DeepSeek R1 costs less per token; Kimi K2.7 Code holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, 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 R1 or Kimi K2.7 Code 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, DeepSeek R1 leans toward open-weight reasoning model 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 R1 or Kimi K2.7 Code?
DeepSeek R1 is cheaper — $0.55/$2.19 per 1M tokens vs $0.95/$4 per 1M tokens, roughly 1.7× apart on input.
Which has the bigger context window?
Kimi K2.7 Code — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek R1 and Kimi K2.7 Code together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, 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 R1 or Kimi K2.7 Code?
Kimi K2.7 Code — released June 12, 2026, about 17 months after DeepSeek R1.
DeepSeek R1 vs Kimi K2.7 Code
DeepSeek · China | Moonshot AI · China · Updated June 2026
Quick verdict
Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. 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 R1 is the value pick.
DeepSeek R1 (DeepSeek) and Kimi K2.7 Code (Moonshot AI) are two of the models people most often weigh against each other in 2026. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. 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 R1 is about 1.7× cheaper on input ($0.55/$2.19 per 1M tokens vs $0.95/$4 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: Kimi K2.7 Code holds 2× more — 256K (~393 pages) vs 128K (~192 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 17 months (released June 12, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek R1
Kimi K2.7 Code
Provider
DeepSeek (China)
Moonshot AI (China)
Released
January 2025
June 12, 2026
Context window
128K (~192 pages)
256K (~393 pages)
Price (in/out)
$0.55/$2.19 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
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open-weight reasoning model
DeepSeek R1
A core design strength of DeepSeek R1.
Transparent chain-of-thought
DeepSeek R1
A core design strength of DeepSeek R1.
Low cost
DeepSeek R1
A core design strength of DeepSeek R1.
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 R1
At $0.55/$2.19 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Kimi K2.7 Code
Its 256K window is about 2× larger, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ DeepSeek R1
At $0.55/$2.19 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
→ Kimi K2.7 Code
Larger 256K window fits more in one prompt.
Anyone whose priority is open-weight reasoning model
→ DeepSeek R1
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 R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs are real: older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 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 R1 and Kimi K2.7 Code overlap enough that the right pick depends on your specific job. DeepSeek R1 costs less per token; Kimi K2.7 Code holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, 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 R1 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 R1 or Kimi K2.7 Code 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, DeepSeek R1 leans toward open-weight reasoning model 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 R1 or Kimi K2.7 Code?
DeepSeek R1 is cheaper — $0.55/$2.19 per 1M tokens vs $0.95/$4 per 1M tokens, roughly 1.7× apart on input.
Which has the bigger context window?
Kimi K2.7 Code — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek R1 and Kimi K2.7 Code together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, 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 R1 or Kimi K2.7 Code?
Kimi K2.7 Code — released June 12, 2026, about 17 months after DeepSeek R1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.