Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. 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 V4 is the value pick.
DeepSeek V4 (DeepSeek) and Kimi K3 (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 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 V4 is about 6.9× cheaper on input ($0.435/$0.87 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: 1M vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
Recency: Kimi K3 is the newer model by about 3 months (released July 27, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek V4
Kimi K3
Provider
DeepSeek (China)
Moonshot AI (China)
Released
April 24, 2026
July 27, 2026
Context window
1M (~1,500 pages)
1M (~1,573 pages)
Price (in/out)
$0.435/$0.87 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
80.6%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Near-frontier coding at ~1/12 the cost: DeepSeek V4 — At $0.435/$0.87 per 1M tokens it undercuts Kimi K3 ($3/$15 per 1M tokens), and that gap compounds at volume.
Open MIT-licensed weights you can self-host: DeepSeek V4 — Kimi K3 is comparatively weak here — 2.8T params need serious hardware to self-host — weights are free, running is not
No long-context surcharge: DeepSeek V4 — China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost — and it runs cheaper at $0.435/$0.87 per 1M tokens.
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 is the newer of the two.
1M-token context with native vision (text, image and video): Kimi K3 — Kimi K3 lists 1M-token context with native vision (text, image and video) among its strengths; DeepSeek V4 does not.
Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness: Kimi K3 — Kimi K3 lists vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness among its strengths; DeepSeek V4 does not.
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.
Which should you pick?
A cost-sensitive startup shipping high volume: DeepSeek V4 — At $0.435/$0.87 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 near-frontier coding at ~1/12 the cost: DeepSeek V4 — 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 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 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 V4 and Kimi K3 overlap enough that the right pick depends on your specific job. DeepSeek V4 costs less per token; Kimi K3 holds the larger context; and each leads in its own area — DeepSeek V4 for near-frontier coding at ~1/12 the cost, 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 V4 or Kimi K3 better for coding?
Public SWE-Bench figures are not available for Kimi K3, 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 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 V4 or Kimi K3?
DeepSeek V4 is cheaper — $0.435/$0.87 per 1M tokens vs $3/$15 per 1M tokens, roughly 6.9× apart on input.
Which has the bigger context window?
Effectively neither — 1M vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both DeepSeek V4 and Kimi K3 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4, 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 V4 or Kimi K3?
Kimi K3 — released July 27, 2026, about 3 months after DeepSeek V4.
DeepSeek V4 vs Kimi K3
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 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 V4 is the value pick.
DeepSeek V4 (DeepSeek) and Kimi K3 (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 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 V4 is about 6.9× cheaper on input ($0.435/$0.87 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: 1M vs 1M — within a few percent of each other, so treat this as a tie and test on your own long inputs, since usable recall varies by model.
▸Recency: Kimi K3 is the newer model by about 3 months (released July 27, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek V4
Kimi K3
Provider
DeepSeek (China)
Moonshot AI (China)
Released
April 24, 2026
July 27, 2026
Context window
1M (~1,500 pages)
1M (~1,573 pages)
Price (in/out)
$0.435/$0.87 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
80.6%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Near-frontier coding at ~1/12 the cost
DeepSeek V4
At $0.435/$0.87 per 1M tokens it undercuts Kimi K3 ($3/$15 per 1M tokens), and that gap compounds at volume.
Open MIT-licensed weights you can self-host
DeepSeek V4
Kimi K3 is comparatively weak here — 2.8T params need serious hardware to self-host — weights are free, running is not
No long-context surcharge
DeepSeek V4
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost — and it runs cheaper at $0.435/$0.87 per 1M tokens.
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 is the newer of the two.
1M-token context with native vision (text, image and video)
Kimi K3
Kimi K3 lists 1M-token context with native vision (text, image and video) among its strengths; DeepSeek V4 does not.
Vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness
Kimi K3
Kimi K3 lists vendor reports 81.2 FrontierSWE and 88.3 Terminal-Bench 2.1 on its own harness among its strengths; DeepSeek V4 does not.
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.
Which should you pick?
A cost-sensitive startup shipping high volume
→ DeepSeek V4
At $0.435/$0.87 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 near-frontier coding at ~1/12 the cost
→ DeepSeek V4
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 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 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 V4 and Kimi K3 overlap enough that the right pick depends on your specific job. DeepSeek V4 costs less per token; Kimi K3 holds the larger context; and each leads in its own area — DeepSeek V4 for near-frontier coding at ~1/12 the cost, 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 V4 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 Kimi K3, 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 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 V4 or Kimi K3?
DeepSeek V4 is cheaper — $0.435/$0.87 per 1M tokens vs $3/$15 per 1M tokens, roughly 6.9× apart on input.
Which has the bigger context window?
Effectively neither — 1M vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both DeepSeek V4 and Kimi K3 together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4, 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 V4 or Kimi K3?
Kimi K3 — released July 27, 2026, about 3 months 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.