Models

gemini-2.5-flash-lite-preview-09-2025-nothink vs Kimi K3

Output tokens cost $0.40 per million on gemini-2.5-flash-lite-preview-09-2025-nothink and $15.00 per million on Kimi K3. Input tokens cost $0.10 per million on gemini-2.5-flash-lite-preview-09-2025-nothink and $3.00 per million on Kimi K3. Cached input tokens are billed at $0.01 per million on gemini-2.5-flash-lite-preview-09-2025-nothink and $0.30 per million on Kimi K3. Context lengths are 1,048,576 tokens on gemini-2.5-flash-lite-preview-09-2025-nothink and 1,048,576 tokens on Kimi K3. Time to first token (TTFT) measured on AIHubMix is 0.8s on gemini-2.5-flash-lite-preview-09-2025-nothink and 0.9s on Kimi K3. Measured output throughput is 204.8 tok/s on gemini-2.5-flash-lite-preview-09-2025-nothink and 73.1 tok/s on Kimi K3.

Googlegemini-2.5-flash-lite-preview-09-2025-nothinkMoonshot AIKimi K3
Google logo
gemini-2.5-flash-lite-preview-09-2025-nothink
Google · text, image, audio, video → text

gemini-2.5-flash-lite latest preview version

Input$0.10 /M
Output$0.40 /M
Moonshot AI logo
Kimi K3
Moonshot AI · text, image, video → text

Kimi K3 is Kimi’s flagship model for long-horizon coding and end-to-end knowledge work, with a 1M-token context window and industry-leading intelligence.

Input$3.00 /M
Output$15.00 /M

Pricing & Specifications

Prices are per million tokens. Time to First Token and throughput are rolling averages measured on AIHubMix.

gemini-2.5-flash-lite-preview-09-2025-nothink
Kimi K3
Input /M
$0.10
$3.00
Output /M
$0.40
$15.00
Cache read /M
$0.01
$0.30
Context length
1,048,576
1,048,576
Max output
65,536
1,048,576
Time to First Token
0.8 s
0.9 s
Throughput
204.8 tok/s
73.1 tok/s
Modalities
textimageaudiovideo
textimagevideo
Supported Parameters
toolsfunction callingstructured outputs
thinkingfunction callingstructured outputs
API Formats
Released
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-

Promotional prices show the discounted rate; see each model page for promotion windows.

Activity Past 30 Days

Daily traffic served through AIHubMix — how demand for each model is trending.

gemini-2.5-flash-lite-preview-09-2025-nothinkkimi-k3

Tokens / day

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Requests / day

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Performance Past 3 Days

Measured on real AIHubMix traffic, hourly buckets. Gaps mean no traffic in that hour.

gemini-2.5-flash-lite-preview-09-2025-nothinkkimi-k3

Throughput (tok/s)

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TTFT (s)

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Uptime (%)

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Cost calculator

Estimate your monthly bill for the same workload on each model.

gemini-2.5-flash-lite-preview-09-2025-nothink
$12.00 /mo
Kimi K3
$405 /mo

Monthly = daily × 30. Discounted rates applied where a promotion is active.

FAQ

Which is cheaper: gemini-2.5-flash-lite-preview-09-2025-nothink, Kimi K3?

gemini-2.5-flash-lite-preview-09-2025-nothink: $0.40/M output tokens; Kimi K3: $15.00/M. Use the cost calculator above to estimate your own workload.

Which responds faster?

gemini-2.5-flash-lite-preview-09-2025-nothink: 0.8s time to first token measured on AIHubMix; see the live performance charts above for how each model behaves across the day.

How large is each context window?

gemini-2.5-flash-lite-preview-09-2025-nothink accepts 1,048,576 and Kimi K3 accepts 1,048,576 input tokens. Maximum output per request is 65,536 tokens on gemini-2.5-flash-lite-preview-09-2025-nothink and 1,048,576 tokens on Kimi K3.

Which one generates tokens faster?

gemini-2.5-flash-lite-preview-09-2025-nothink at 204.8 tok/s and Kimi K3 at 73.1 tok/s, measured as output throughput on AIHubMix — a separate metric from time to first token.

What inputs and capabilities does each model support?

gemini-2.5-flash-lite-preview-09-2025-nothink accepts text, image, audio and video input and supports tool calling, function calling and structured outputs; Kimi K3 accepts text, image and video input and supports thinking, function calling and structured outputs.

Can I call gemini-2.5-flash-lite-preview-09-2025-nothink and Kimi K3 with the same API key?

Yes. AIHubMix serves every model on this page behind one OpenAI-compatible endpoint, so switching between them is a one-line change to the model field — no second account, key or SDK.

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