Gemini 2.0 Flash vs Kimi K2.6 vs Claude Sonnet 3.5 v2
Kimi K2.6 comes out ahead, 74 to 65 and 54 on our weighted score, and it is the cheaper option too.
GoogleGemini 2.0 Flash
65/100- ECI134.7
- Price—
- Context1.05M
- Our pick
Moonshot AIKimi K2.6
74/100- ECI151.1
- Price$0.95 / $4.00
- Context262K
AnthropicClaude Sonnet 3.5 v2
54/100- ECI133.5
- Price$3.00 / $15.00
- Context200K
Kimi K2.6 is our pick
Kimi K2.6 is the better all-round choice, scoring 74/100 against Gemini 2.0 Flash (65) and Claude Sonnet 3.5 v2 (54). It leads on capability. Gemini 2.0 Flash wins on inputs & features and context window. The score weighs capability 67%, inputs & features 20%, context window 13%.
- CapabilityKimi K2.6Capabilities Index (ECI): Kimi K2.6 151.1 · Gemini 2.0 Flash 134.7 · Claude Sonnet 3.5 v2 133.5
- Lowest priceKimi K2.6Kimi K2.6 $1.71 · Claude Sonnet 3.5 v2 $6.00 per 1M tokens (3:1 blend) · Gemini 2.0 Flash unpriced
- Longest contextGemini 2.0 FlashGemini 2.0 Flash 1,048,576 · Kimi K2.6 262,144 · Claude Sonnet 3.5 v2 200,000 tokens
- Widest inputsGemini 2.0 FlashGemini 2.0 Flash: Text, Images, PDFs, Audio, Video · Kimi K2.6: Text, Images, Video · Claude Sonnet 3.5 v2: Text, Images, PDFs
- Self-hostingKimi K2.6Publishes downloadable weights
| Measure | Weight | Gemini 2.0 Flash | Kimi K2.6 | Claude Sonnet 3.5 v2 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 67% | 59 | 79 | 57 |
| Inputs & features | 20% | 90 | 80 | 60 |
| Context window | 13% | 61 | 37 | 32 |
| Overall | 100% | 65/100 | 74/100 | 54/100 |
Left out because at least one model lacks the data: price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemini 2.0 FlashGoogle | Kimi K2.6Moonshot AI | Claude Sonnet 3.5 v2Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 134.7 | 151.1 (best) | 133.5 |
| ECI rank | #116 of 148 | #45 of 148 (best) | #119 of 148 |
| GPQA DiamondGraduate-level science questions | — | 90.8% (best) | 55.3% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 57.2% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 96.1% (best) | 8.5% |
| SWE-bench VerifiedFixing real GitHub issues | — | 76.7% | — |
| SimpleQA VerifiedShort factual questions | — | 34.9% | — |
| Price per million tokens | |||
| Input | — | $0.95 (best) | $3.00 |
| Output | — | $4.00 (best) | $15.00 |
| Cached input | — | $0.16 | — |
| Blended (3:1) | — | $1.71 (best) | $6.00 |
| Long-context rate | — | Same rate | Same rate |
| Price source | — | Official Moonshot AI API | Median of 1 providers |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 262,144 tokens | 200,000 tokens |
| Max output | 8,192 tokens | 262,144 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | Yes | No | Yes |
| Audio | Yes | No | No |
| Video | Yes | Yes | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | — | kimi-k2.6 | — |
| API providers | — | 46 (best) | 1 |
| Released | Dec 11, 2024 | Apr 21, 2026 | Oct 22, 2024 |
| Knowledge cutoff | Jun 2024 | Jan 2025 | Apr 30, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Gemini 2.0 Flash—
- Kimi K2.6$17.50
- Claude Sonnet 3.5 v2$60.00
Which should you choose?
Which is better: Gemini 2.0 Flash, Kimi K2.6 or Claude Sonnet 3.5 v2?
Kimi K2.6 is the better all-round choice, scoring 74/100 against Gemini 2.0 Flash (65) and Claude Sonnet 3.5 v2 (54). It leads on capability. Gemini 2.0 Flash wins on inputs & features and context window. The score weighs capability 67%, inputs & features 20%, context window 13%.
Which is cheaper, Gemini 2.0 Flash, Kimi K2.6 or Claude Sonnet 3.5 v2?
Kimi K2.6 is cheaper at $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). Claude Sonnet 3.5 v2 costs $3.00 input / $15.00 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $1.71 per million tokens for Kimi K2.6 versus $6.00 for Claude Sonnet 3.5 v2 (3.5× as much). Gemini 2.0 Flash has no published per-token price.
Which scores higher on benchmarks?
Kimi K2.6 scores higher on the Capabilities Index (ECI): Kimi K2.6 151.1 (#45 of 148), Gemini 2.0 Flash 134.7 (#116 of 148) and Claude Sonnet 3.5 v2 133.5 (#119 of 148). Their confidence ranges do not overlap (149.1–152.8 vs 124.3–136.7), so the gap is a real one.
Which is better for coding?
There are no published SWE-bench Verified results for Gemini 2.0 Flash and Claude Sonnet 3.5 v2 yet, so there is no like-for-like coding score. On overall capability, Kimi K2.6 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Gemini 2.0 Flash has the largest context window at 1,048,576 tokens, against 262,144 for Kimi K2.6 and 200,000 for Claude Sonnet 3.5 v2. Maximum output per response: Gemini 2.0 Flash up to 8,192, Kimi K2.6 up to 262,144, Claude Sonnet 3.5 v2 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Gemini 2.0 Flash accepts text, images, PDFs, audio and video; Kimi K2.6 accepts text, images and video; Claude Sonnet 3.5 v2 accepts text, images and PDFs. Gemini 2.0 Flash handles the widest range of inputs.
Are any of these open source?
Kimi K2.6 publishes its weights and can be self-hosted; Gemini 2.0 Flash and Claude Sonnet 3.5 v2 is proprietary.
Which is newer?
Kimi K2.6 is the newest, released Apr 21, 2026. Gemini 2.0 Flash came out Dec 11, 2024; Claude Sonnet 3.5 v2 came out Oct 22, 2024. Knowledge cutoff: Gemini 2.0 Flash Jun 2024, Kimi K2.6 Jan 2025, Claude Sonnet 3.5 v2 Apr 30, 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
