GPT-5 Nano vs Kimi K3

Pick up to 3 models · pricing, context and capabilities · simulate your product cost

Data updated on September 30, 2026· 217 models

OpenAIGPT-5 Nano

GPT-5-Nano is the smallest and fastest variant in the GPT-5 system, optimized for developer tools, rapid interactions, and ultra-low latency environments.

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Reasoning estimated
Speed estimated
▲
Input
Output
Context
400 K tokens
Max output
128 K
Input, $ per 1M tokens
▲$0.050
Output, $ per 1M tokens
▲$0.400
Cache read, $ per 1M
$0.005
Batch (input / output)
$0.025 / $0.200
Tool calling
Yes
Released
08/07/2025
Knowledge cutoff
2024-05-30
API id
openai/gpt-5-nano
MoonshotKimi K3

Kimi K3 is a 2.8T parameter open-weight multimodal reasoning model from Moonshot AI.

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Reasoning estimated
▲
Speed estimated
Input
Output
Context
▲1 M tokens
Max output
▲944 K
Input, $ per 1M tokens
$3.00
Output, $ per 1M tokens
$15.00
Cache read, $ per 1M
$0.300
Batch (input / output)
$2.28 / $11.40
Tool calling
Yes
Released
▲07/16/2026
Knowledge cutoff
—
API id
moonshotai/kimi-k3
Third model (optional)
text image audio video PDF / file reasoning (1 to 5, estimated by family) speed (1 to 4, estimated by family)▲ best value among the selected

Which is the better API choice: GPT-5 Nano vs Kimi K3?

  • GPT-5 Nano: cheapest input (98% less than Kimi K3)
  • GPT-5 Nano: cheapest output
  • Kimi K3: largest context (1 M)
  • Kimi K3: the newest (07/16/2026)
  • Kimi K3: most reasoning (estimated)
  • GPT-5 Nano: the fastest (estimated)

GPT-5 Nano costs 98% less on input and 97% less on output than Kimi K3 ($0.050 / $0.400 versus $3.00 / $15.00 per million tokens). Kimi K3 accepts more context (1 M). GPT-5 Nano and Kimi K3 offer a batch tier at half price for jobs that do not need an immediate answer.

What would your product cost?

Estimate the monthly cost with your app's real volume. List prices, no cache or batch discounts.

GPT-5 Nano
$2.35/mo
Kimi K3
$105.00/mo

With GPT-5 Nano you save $102.65 a month versus Kimi K3 (98% less).

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Paste your prompt: tokens and cost per model

Rough estimate (about 4 characters per token). Nothing is sent to any server.

76tokens approx.287 characters

Cost of sending it 1,000 times (input only)

  1. 1GPT-5 Nano OpenAI$0.004
  2. 2Claude Sonnet 5.5 Anthropic$0.152
  3. 3Claude Sonnet 5 Anthropic$0.152
  4. 4Kimi K3 Moonshot$0.228
  5. 5Claude Opus 5.5 Anthropic$0.304
  6. 6Claude Opus 5 Anthropic$0.380
  7. 7Claude Fable 5.1 Anthropic$0.760
  8. 8Claude Fable 5 Anthropic$0.760

Popular comparisons

Frequently asked questions

Which is the better API choice: GPT-5 Nano vs Kimi K3?

GPT-5 Nano costs 98% less on input and 97% less on output than Kimi K3 ($0.050 / $0.400 versus $3.00 / $15.00 per million tokens). Kimi K3 accepts more context (1 M). GPT-5 Nano and Kimi K3 offer a batch tier at half price for jobs that do not need an immediate answer.

Which is cheaper: GPT-5 Nano and Kimi K3?

GPT-5 Nano is cheaper: $0.050 per million input tokens and $0.400 per million output tokens, versus $3.00 / $15.00 for Kimi K3.

Which has more context: GPT-5 Nano and Kimi K3?

Kimi K3 accepts 1 M tokens of context; GPT-5 Nano accepts 400 K.

Which is newer: GPT-5 Nano and Kimi K3?

Kimi K3 was released on 07/16/2026; GPT-5 Nano on 08/07/2025.

What would 10,000 requests a month cost with GPT-5 Nano and Kimi K3?

With 1,500 input and 400 output tokens per request: GPT-5 Nano: $2.35; Kimi K3: $105.00 a month, at list price.

Where do the prices come from and how often are they updated?

From the public catalogs of OpenRouter, models.dev and LiteLLM, which publish each provider's list prices. They sync once a day and the last update date is shown above the comparison.

What does price per million tokens mean?

It is what the provider charges per million tokens you send (input) or the model generates (output). A token is roughly 4 characters. Output almost always costs more than input.

Sources: OpenRouter, models.dev and LiteLLM. Prices in USD per million tokens, list price, no volume discounts. Prompt tokens are an estimate; each provider's tokenizer may differ.