Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. Pick Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable or 1m-token context with native vision (text, image and video). On a tight budget at scale, DeepSeek R1 is the value pick.
DeepSeek R1 (DeepSeek) and Kimi K3 (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 K3 is moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Price: DeepSeek R1 is about 5.5× cheaper on input ($0.55/$2.19 per 1M tokens vs $3/$15 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: Kimi K3 holds 8.2× more — 1M (~1,573 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 K3 is the newer model by about 18 months (released July 27, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek R1
Kimi K3
Provider
DeepSeek (China)
Moonshot AI (China)
Released
January 2025
July 27, 2026
Context window
128K (~192 pages)
1M (~1,573 pages)
Price (in/out)
$0.55/$2.19 per 1M tokens
$3/$15 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 — Kimi K3 is comparatively weak here — 2.8T params need serious hardware to self-host — weights are free, running is not
Transparent chain-of-thought: DeepSeek R1 — The open-weight reasoning model that reset price expectations in early 2025 — and it runs cheaper at $0.55/$2.19 per 1M tokens.
Low cost: DeepSeek R1 — At $0.55/$2.19 per 1M tokens it undercuts Kimi K3 ($3/$15 per 1M tokens), and that gap compounds at volume.
Largest open-weight model at release — 2.8T sparse MoE, self-hostable: Kimi K3 — Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores — and it carries the larger 1M context.
1M-token context with native vision (text, image and video): Kimi K3 — Its 1M window holds about 8.2× more than DeepSeek R1's 128K in a single prompt.
Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness: Kimi K3 — Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores — and it is the newer of the two.
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 K3 — Its 1M window is about 8.2× larger than DeepSeek R1's 128K, fitting roughly 1,573 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 K3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Kimi K3 — Larger 1M 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 largest open-weight model at release — 2.8t sparse moe, self-hostable: Kimi K3 — 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 K3: where it fits
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. Released July 27, 2026 by Moonshot AI, it is built for largest open-weight model at release — 2.8T sparse MoE, self-hostable, 1M-token context with native vision (text, image and video), vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness, and fresh-input pricing of $3/M (cached $0.30/M), flat across the full 1M context.
Its trade-offs: coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified, 2.8T params need serious hardware to self-host — weights are free, running is not, no independent benchmark reproduction yet at release, and image input but no audio or video. At $3 in / $15 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
DeepSeek R1 and Kimi K3 overlap enough that the right pick depends on your specific job. DeepSeek R1 costs less per token; Kimi K3 holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable. 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 K3 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 K3 leans toward largest open-weight model at release — 2.8t sparse moe, self-hostable, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek R1 or Kimi K3?
DeepSeek R1 is cheaper — $0.55/$2.19 per 1M tokens vs $3/$15 per 1M tokens, roughly 5.5× apart on input.
Which has the bigger context window?
Kimi K3 — 1M vs 128K, about 8.2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek R1 and Kimi K3 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, Kimi K3 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 K3?
Kimi K3 — released July 27, 2026, about 18 months after DeepSeek R1.
DeepSeek R1 vs Kimi K3
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 K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable or 1m-token context with native vision (text, image and video). On a tight budget at scale, DeepSeek R1 is the value pick.
DeepSeek R1 (DeepSeek) and Kimi K3 (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 K3 is moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. 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 5.5× cheaper on input ($0.55/$2.19 per 1M tokens vs $3/$15 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: Kimi K3 holds 8.2× more — 1M (~1,573 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 K3 is the newer model by about 18 months (released July 27, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek R1
Kimi K3
Provider
DeepSeek (China)
Moonshot AI (China)
Released
January 2025
July 27, 2026
Context window
128K (~192 pages)
1M (~1,573 pages)
Price (in/out)
$0.55/$2.19 per 1M tokens
$3/$15 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
Kimi K3 is comparatively weak here — 2.8T params need serious hardware to self-host — weights are free, running is not
Transparent chain-of-thought
DeepSeek R1
The open-weight reasoning model that reset price expectations in early 2025 — and it runs cheaper at $0.55/$2.19 per 1M tokens.
Low cost
DeepSeek R1
At $0.55/$2.19 per 1M tokens it undercuts Kimi K3 ($3/$15 per 1M tokens), and that gap compounds at volume.
Largest open-weight model at release — 2.8T sparse MoE, self-hostable
Kimi K3
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores — and it carries the larger 1M context.
1M-token context with native vision (text, image and video)
Kimi K3
Its 1M window holds about 8.2× more than DeepSeek R1's 128K in a single prompt.
Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness
Kimi K3
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores — and it is the newer of the two.
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 K3
Its 1M window is about 8.2× larger than DeepSeek R1's 128K, fitting roughly 1,573 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 K3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Kimi K3
Larger 1M 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 largest open-weight model at release — 2.8t sparse moe, self-hostable
→ Kimi K3
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 K3: where it fits
Moonshot's 2.8-trillion-parameter open-weight model — the largest open AI at release, with a 1M context and strong self-reported coding scores. Released July 27, 2026 by Moonshot AI, it is built for largest open-weight model at release — 2.8T sparse MoE, self-hostable, 1M-token context with native vision (text, image and video), vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness, and fresh-input pricing of $3/M (cached $0.30/M), flat across the full 1M context.
Its trade-offs: coding scores use Moonshot FrontierSWE, not standard SWE-Bench Verified, 2.8T params need serious hardware to self-host — weights are free, running is not, no independent benchmark reproduction yet at release, and image input but no audio or video. At $3 in / $15 out per million tokens, it sits in the mid price band.
The bottom line for this matchup
DeepSeek R1 and Kimi K3 overlap enough that the right pick depends on your specific job. DeepSeek R1 costs less per token; Kimi K3 holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, Kimi K3 for largest open-weight model at release — 2.8t sparse moe, self-hostable. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both DeepSeek R1 and Kimi K3 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.
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 K3 leans toward largest open-weight model at release — 2.8t sparse moe, self-hostable, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek R1 or Kimi K3?
DeepSeek R1 is cheaper — $0.55/$2.19 per 1M tokens vs $3/$15 per 1M tokens, roughly 5.5× apart on input.
Which has the bigger context window?
Kimi K3 — 1M vs 128K, about 8.2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek R1 and Kimi K3 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, Kimi K3 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 K3?
Kimi K3 — released July 27, 2026, about 18 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.