GPT-6.1 Sol Pro vs Kimi K2.7 Code
Pick up to 3 models · pricing, context and capabilities · simulate your product cost
Data updated on October 23, 2026· 222 models
GPT-6.1 Sol Pro is the same underlying model as GPT-6.1 Sol, served with reasoning.mode set to pro for higher-quality responses on complex tasks.
- Reasoning estimated
- ▲
- Speed estimated
- Input
- Output
- Context
- ▲1.1 M tokens
- Max output
- 128 K
- Input, $ per 1M tokens
- $2.00
- Output, $ per 1M tokens
- $10.00
- Cache read, $ per 1M
- $0.100
- Batch (input / output)
- $1.00 / $5.00
- Tool calling
- Yes
- Released
- ▲09/29/2026
- Knowledge cutoff
- —
- API id
- openai/gpt-6.1-sol-pro
MoonshotAI: Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts.
- Reasoning estimated
- Speed estimated
- ▲
- Input
- Output
- Context
- 262 K tokens
- Max output
- ▲236 K
- Input, $ per 1M tokens
- ▲$0.671
- Output, $ per 1M tokens
- ▲$3.35
- Cache read, $ per 1M
- $0.180
- Batch (input / output)
- —
- Tool calling
- Yes
- Released
- 06/12/2026
- Knowledge cutoff
- 2025-01
- API id
- moonshotai/kimi-k2.7-code
Which is the better API choice: GPT-6.1 Sol Pro vs Kimi K2.7 Code?
- Kimi K2.7 Code: cheapest input (66% less than GPT-6.1 Sol Pro)
- Kimi K2.7 Code: cheapest output
- GPT-6.1 Sol Pro: largest context (1.1 M)
- GPT-6.1 Sol Pro: the newest (09/29/2026)
- GPT-6.1 Sol Pro: most reasoning (estimated)
- Kimi K2.7 Code: the fastest (estimated)
Kimi K2.7 Code costs 66% less on input and 67% less on output than GPT-6.1 Sol Pro ($0.671 / $3.35 versus $2.00 / $10.00 per million tokens). GPT-6.1 Sol Pro accepts more context (1.1 M). GPT-6.1 Sol Pro offers 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.
With Kimi K2.7 Code you save $46.53 a month versus GPT-6.1 Sol Pro (66% 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.
Cost of sending it 1,000 times (input only)
- 1Claude Haiku 5.5 Anthropic$0.008
- 2Kimi K2.7 Code Moonshot$0.051
- 3GPT-6.1 Sol Pro OpenAI$0.152
- 4Claude Sonnet 5.5 Anthropic$0.152
- 5Claude Sonnet 5 Anthropic$0.152
- 6Claude Opus 5.5 Anthropic$0.304
- 7Claude Opus 5 Anthropic$0.380
- 8Claude Fable 5.1 Anthropic$0.760
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Frequently asked questions
Which is the better API choice: GPT-6.1 Sol Pro vs Kimi K2.7 Code?
Kimi K2.7 Code costs 66% less on input and 67% less on output than GPT-6.1 Sol Pro ($0.671 / $3.35 versus $2.00 / $10.00 per million tokens). GPT-6.1 Sol Pro accepts more context (1.1 M). GPT-6.1 Sol Pro offers a batch tier at half price for jobs that do not need an immediate answer.
Which is cheaper: GPT-6.1 Sol Pro and Kimi K2.7 Code?
Kimi K2.7 Code is cheaper: $0.671 per million input tokens and $3.35 per million output tokens, versus $2.00 / $10.00 for GPT-6.1 Sol Pro.
Which has more context: GPT-6.1 Sol Pro and Kimi K2.7 Code?
GPT-6.1 Sol Pro accepts 1.1 M tokens of context; Kimi K2.7 Code accepts 262 K.
Which is newer: GPT-6.1 Sol Pro and Kimi K2.7 Code?
GPT-6.1 Sol Pro was released on 09/29/2026; Kimi K2.7 Code on 06/12/2026.
What would 10,000 requests a month cost with GPT-6.1 Sol Pro and Kimi K2.7 Code?
With 1,500 input and 400 output tokens per request: GPT-6.1 Sol Pro: $70.00; Kimi K2.7 Code: $23.47 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.