Model comparison
Gemini 3.1 Pro vs Kimi K3
Google against Moonshot AI, compared on context, price, and verified benchmark results.
Catalog record checked August 9, 2026; individual provider fields may change.
Gemini 3.1 Pro
Frontier
Moonshot AI
Kimi K3
Frontier · Open weights
| Specification | Gemini 3.1 Pro | Kimi K3 |
|---|---|---|
| Provider | Moonshot AI | |
| Tier | Frontier | Frontier |
| Context window | 1.05M | 1.05M |
| Max output | 66K | Winner: 1M |
| Input / 1M tokens | Winner: $2 | $3 |
| Output / 1M tokens | Winner: $12 | $15 |
| Weights | Closed | Open |
| Parameters | Not disclosed | 2.8T total / 104B active (MoE) |
| Reasoning levels | Not verified | low, high, max |
| Modalities | text, image, video, audio, pdf | text, image, video |
| Released | February 19, 2026 | July 16, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-08-09) | 48 | Winner: 60 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Pricing tiers: Gemini 3.1 Pro: Base tier is $2/$12 per million tokens; prompts above 200K tokens are priced at $4/$18. Prefer the Preview API id `gemini-3.1-pro-preview` (not a GA alias).
Gemini 3.1 Pro
Google's Pro tier in limited preview since 2026-02-19, with a 1.05M-token context window and multimodal input.
Best for
- Very long documents
- Multimodal input
- Google Workspace integration
Watch out
Scored 12% on DeepSWE, far below every other model here including budget tiers — weak on agentic coding specifically. Pricing also steps up to $4/$18 above 200K tokens. Wire calls to `gemini-3.1-pro-preview`.
Kimi K3
Open-weight frontier model scoring within a few points of the closed leaders on agentic coding.
Best for
- Agentic coding without vendor lock-in
- Self-hosting at frontier quality
- Long-context work
Watch out
2.8T parameters means self-hosting is a datacentre exercise, not a workstation one — open weights here mean provider choice, not local inference.
Benchmark
DeepSWE 1.1 in context
Both models shown against the wider field, with cost per completed task alongside the score.
Local leader
Claude Opus 5 [max]
74%
Rows shown
24
Highest published reasoning effort per model (not best Pass@1)
Snapshot date
2026-08-13
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost / tokens / steps; v1 uses published medians.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 [max] | 74% | $11.84 | 118k | 99 |
| 2 | GPT-5.6 Sol [max] | 73% | $8.39 | 60k | 61 |
| 3 | Claude Fable 5 [max] | 70% | $21.63 | 119k | 88 |
| 4 | GPT-5.6 Terra [max] | 70% | $4.95 | 72k | 76 |
| 5 | Kimi K3 [max] | 69% | $4.65 | 82k | 98 |
| 6 | GPT-5.6 Luna [max] | 67% | $3.03 | 73k | 102 |
| 7 | GPT-5.5 [xhigh] | 67% | $7.23 | 46k | 82 |
| 8 | Grok 4.6 [xhigh] | 67% | $5.50 | 71k | 87 |
| 9 | Gemini 3.7 Flash [high] | 65% | $2.18 | 107k | 125 |
| 10 | DeepSeek V4-Pro [max] | 63% | $0.24 | 106k | 155 |
| 11 | Claude Opus 4.8 [max] | 59% | $13.22 | 135k | 120 |
| 12 | Qwen3.8-Max [xhigh] | 58% | $3.73 | 95k | 111 |
| 13 | Muse Spark 1.2 [xhigh] | 55% | $3.70 | 99k | 101 |
| 14 | Claude Sonnet 5 [max] | 54% | $26.40 | 214k | 268 |
| 15 | Grok 4.5 [high] | 54% | $2.42 | 36k | 61 |
| 16 | DeepSeek V4-Flash [max] | 53% | $0.10 | 108k | 153 |
| 17 | Muse Spark 1.1 [xhigh] | 53% | $2.36 | 74k | 96 |
| 18 | GPT-5.4 [xhigh] | 52% | $5.65 | 71k | 70 |
| 19 | Gemini 3.6 Flash [high] | 47% | $4.42 | 96k | 117 |
| 20 | GLM 5.2 [max] | 44% | $3.92 | 78k | 129 |
| 21 | Gemini 3.5 Flash [high] | 36% | $3.45 | 76k | 105 |
| 22 | Kimi K2.7 Code | 31% | $2.82 | 59k | 149 |
| 23 | Claude Sonnet 4.6 [high] | 30% | $5.52 | 76k | 134 |
| 24 | Gemini 3.1 Pro [high] | 12% | $2.14 | 28k | 76 |
DeepSWE “Best” picks the highest published reasoning effort per model (not the highest pass rate). Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
Common questions
Gemini 3.1 Pro vs Kimi K3
Answered from the verified figures on this page rather than general guidance.
Is Gemini 3.1 Pro or Kimi K3 cheaper for input?
Gemini 3.1 Pro is cheaper at $2 per million input tokens, against $3 for Kimi K3 — roughly 1.5× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Gemini 3.1 Pro has tiered pricing: Base tier is $2/$12 per million tokens; prompts above 200K tokens are priced at $4/$18. Prefer the Preview API id `gemini-3.1-pro-preview` (not a GA alias).
Is Gemini 3.1 Pro or Kimi K3 cheaper for output?
Gemini 3.1 Pro is cheaper at $12 per million output tokens, against $15 for Kimi K3 — roughly 1.3× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Gemini 3.1 Pro has tiered pricing: Base tier is $2/$12 per million tokens; prompts above 200K tokens are priced at $4/$18. Prefer the Preview API id `gemini-3.1-pro-preview` (not a GA alias).
Which has the larger context window, Gemini 3.1 Pro or Kimi K3?
Both accept about 1.05M tokens of context, so document length will not decide between them.
Should I use Gemini 3.1 Pro or Kimi K3?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. Gemini 3.1 Pro suits very long documents; Kimi K3 suits agentic coding without vendor lock-in.
Can I self-host Gemini 3.1 Pro or Kimi K3?
Kimi K3 publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. Gemini 3.1 Pro is a closed model whose supported access paths are controlled by its provider.
Next step
Choosing between them
Tier and workload decide this more reliably than a leaderboard position does.
If both sit in the same tier, the decision usually comes down to context window and output price rather than headline capability — output tokens dominate real bills.
If one is a step up within the same provider, the useful question is whether your hardest task actually fails on the cheaper tier. Most production volume — classification, extraction, summarization — does not.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Muse Code, or Lovable? Compare agentic harnesses · Latest releases.