Pick Gemini 3.1 Pro for full multimodal input — text, image, audio and video in one 1m-token window or long video and document analysis. 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). Choose Kimi K3 if you need self-hosting or data privacy; Gemini 3.1 Pro if you want a managed API.
Gemini 3.1 Pro (Google, US) and Kimi K3 (Moonshot AI, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Gemini 3.1 Pro is a 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. 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 open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: Gemini 3.1 Pro is about 1.5× cheaper on input ($2/$12 per 1M tokens vs $3/$15 per 1M tokens) — modest, but it adds up at steady volume.
Context window: both advertise 1M (~1,573 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Kimi K3 is the newer model by about 5 months (released July 27, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Gemini 3.1 Pro
Kimi K3
Provider
Google (US)
Moonshot AI (China)
Released
February 19, 2026
July 27, 2026
Context window
1M (~1,573 pages)
1M (~1,573 pages)
Price (in/out)
$2/$12 per 1M tokens
$3/$15 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
26.3%
Not published
Who wins what
Full multimodal input — text, image, audio and video in one 1M-token window: Gemini 3.1 Pro — Kimi K3 is comparatively weak here — image input but no audio or video
Long video and document analysis: Gemini 3.1 Pro — A 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window — and it runs cheaper at $2/$12 per 1M tokens.
Agentic reasoning (high ARC-AGI-2): Gemini 3.1 Pro — Gemini 3.1 Pro lists agentic reasoning (high ARC-AGI-2) among its strengths; Kimi K3 does not.
Largest open-weight model at release — 2.8T sparse MoE, self-hostable: Kimi K3 — Open weights make this possible at all — Gemini 3.1 Pro is API-only, so it cannot leave the vendor's servers.
1M-token context with native vision (text, image and video): Kimi K3 — Gemini 3.1 Pro is comparatively weak here — long-context recall drops sharply past 256K (26.3% on MRCR v2 at 1M)
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 its weights are open while Gemini 3.1 Pro is API-only.
Lowest cost at scale: Gemini 3.1 Pro — At $2/$12 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: Gemini 3.1 Pro — At $2/$12 per 1M tokens it undercuts Kimi K3, and on millions of tokens that margin decides the monthly bill.
A team with data-privacy or self-hosting needs: Kimi K3 — Open weights let you run it on your own hardware; Gemini 3.1 Pro is API-only.
Anyone whose priority is full multimodal input — text, image, audio and video in one 1m-token window: Gemini 3.1 Pro — 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.
An enterprise with regional data-residency rules: Gemini 3.1 Pro or Kimi K3 — Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Gemini 3.1 Pro: where it fits
A 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. Released February 19, 2026 by Google, it is built for full multimodal input — text, image, audio and video in one 1M-token window, long video and document analysis, agentic reasoning (high ARC-AGI-2), and broad multimodal understanding across formats.
Its trade-offs are real: long-context recall drops sharply past 256K (26.3% on MRCR v2 at 1M), premium price per token at $2/$12, and superseded within Google's own line by the newer 3.5/3.6 Flash releases for cost-sensitive work. At $2 in / $12 out per million tokens, it sits in the mid 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
The defining split here is open vs. closed. Kimi K3 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 3.1 Pro gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is Gemini 3.1 Pro 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, Gemini 3.1 Pro leans toward full multimodal input — text, image, audio and video in one 1m-token window 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, Gemini 3.1 Pro or Kimi K3?
Kimi K3 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Both advertise 1M (~1,573 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Gemini 3.1 Pro and Kimi K3 together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Pro, 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, Gemini 3.1 Pro or Kimi K3?
Kimi K3 — released July 27, 2026, about 5 months after Gemini 3.1 Pro.
Gemini 3.1 Pro vs Kimi K3
Google · US | Moonshot AI · China · Updated June 2026
Quick verdict
Pick Gemini 3.1 Pro for full multimodal input — text, image, audio and video in one 1m-token window or long video and document analysis. 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). Choose Kimi K3 if you need self-hosting or data privacy; Gemini 3.1 Pro if you want a managed API.
Gemini 3.1 Pro (Google, US) and Kimi K3 (Moonshot AI, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Gemini 3.1 Pro is a 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. 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 open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Price: Gemini 3.1 Pro is about 1.5× cheaper on input ($2/$12 per 1M tokens vs $3/$15 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: both advertise 1M (~1,573 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Recency: Kimi K3 is the newer model by about 5 months (released July 27, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a US-vs-China matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Gemini 3.1 Pro
Kimi K3
Provider
Google (US)
Moonshot AI (China)
Released
February 19, 2026
July 27, 2026
Context window
1M (~1,573 pages)
1M (~1,573 pages)
Price (in/out)
$2/$12 per 1M tokens
$3/$15 per 1M tokens
Open weight?
No — API only
Yes — self-hostable
Modalities
text, image, audio, video, code
text, image, video, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
26.3%
Not published
Who wins what
Full multimodal input — text, image, audio and video in one 1M-token window
Gemini 3.1 Pro
Kimi K3 is comparatively weak here — image input but no audio or video
Long video and document analysis
Gemini 3.1 Pro
A 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window — and it runs cheaper at $2/$12 per 1M tokens.
Agentic reasoning (high ARC-AGI-2)
Gemini 3.1 Pro
Gemini 3.1 Pro lists agentic reasoning (high ARC-AGI-2) among its strengths; Kimi K3 does not.
Largest open-weight model at release — 2.8T sparse MoE, self-hostable
Kimi K3
Open weights make this possible at all — Gemini 3.1 Pro is API-only, so it cannot leave the vendor's servers.
1M-token context with native vision (text, image and video)
Kimi K3
Gemini 3.1 Pro is comparatively weak here — long-context recall drops sharply past 256K (26.3% on MRCR v2 at 1M)
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 its weights are open while Gemini 3.1 Pro is API-only.
Lowest cost at scale
Gemini 3.1 Pro
At $2/$12 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
→ Gemini 3.1 Pro
At $2/$12 per 1M tokens it undercuts Kimi K3, and on millions of tokens that margin decides the monthly bill.
A team with data-privacy or self-hosting needs
→ Kimi K3
Open weights let you run it on your own hardware; Gemini 3.1 Pro is API-only.
Anyone whose priority is full multimodal input — text, image, audio and video in one 1m-token window
→ Gemini 3.1 Pro
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.
An enterprise with regional data-residency rules
→ Gemini 3.1 Pro or Kimi K3
Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Gemini 3.1 Pro: where it fits
A 1M-token multimodal workhorse — huge breadth across text, image, audio and video, but recall fades deep in the window. Released February 19, 2026 by Google, it is built for full multimodal input — text, image, audio and video in one 1M-token window, long video and document analysis, agentic reasoning (high ARC-AGI-2), and broad multimodal understanding across formats.
Its trade-offs are real: long-context recall drops sharply past 256K (26.3% on MRCR v2 at 1M), premium price per token at $2/$12, and superseded within Google's own line by the newer 3.5/3.6 Flash releases for cost-sensitive work. At $2 in / $12 out per million tokens, it sits in the mid 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
The defining split here is open vs. closed. Kimi K3 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 3.1 Pro gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both Gemini 3.1 Pro 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, Gemini 3.1 Pro leans toward full multimodal input — text, image, audio and video in one 1m-token window 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, Gemini 3.1 Pro or Kimi K3?
Kimi K3 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.1 Pro is API-metered at $2/$12 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Both advertise 1M (~1,573 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Gemini 3.1 Pro and Kimi K3 together?
Yes — a multi-model platform like LumiChats gives you Gemini 3.1 Pro, 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, Gemini 3.1 Pro or Kimi K3?
Kimi K3 — released July 27, 2026, about 5 months after Gemini 3.1 Pro.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.