Kimi K3.1's per-token cost is approximately ten times higher than DeepSeek V41 Flash and double the price of GLM 5.3

Overview Debates Related claims Topics

Rippling's coding workflow usage shifted from 57% Anthropic and 16% OpenAI in May to 52% OpenAI and 18% Anthropic following OpenAI price cuts

Claude Sonnet 5.5 achieved strong performance on Leash Bio's chemical exploration benchmark

Claude Sonnet 5 exhibited high operational costs and low performance on Leash Bio's chemical exploration benchmark

Solve 6.1 demonstrated high performance on Leash Bio's chemical exploration benchmark

Ramp data showed Claude Opus 5.5 captured 35.1% of token share and 34.4% of business spend across tracked models

Moonshot released Kimi K3.1 Pro as the first model update to the Kimi K3 family

Artificial Analysis determined that Kimi K3.1 consumes 14% more tokens per task than Kimi K3

Moonshot priced Kimi K3.1 at the same per-token rate as Sonnet 5.5 and GPT6 SOL

Moonshot instructed influencers to promote Kimi K3.1 as matching Opus 5.5 performance at one-fifth the cost

Moonshot's open-weight Kimi K3 model had 2.8 trillion parameters upon its July release

In independent evaluations by Artificial Analysis, Kimi K3.1 scored 55 on the Intelligence Index, ranking fourth behind Opus 5.5, Sonnet 5.5, and Muse

Astra 6 achieved the highest performance among frontier language models evaluated on Leash Bio's chemical exploration benchmark

Moonshot reported that Kimi K3.1 cost approximately $5 per task on Terminalbench 4.0, compared to $15 for Opus 5.5 and $10 for GPT6 Astra

On the Artificial Analysis Intelligence Index, Kimi K3.1 scored two points higher than Fable 5.1, GPT6 Astra, and Gemini 4 Argon

DeepSeek-V4-Flash performed well among open-source models evaluated on Leash Bio's chemical exploration benchmark

Moonshot priced Kimi K3.1 at approximately two-thirds the per-token cost of Kimi K3

OpenRouter data showed that seven-day average token volume for Anthropic models increased 3.6-fold following the release of Claude Opus 5.5

Higher-performing language models abandon unproductive chemical neighborhoods more quickly than lower-performing models during molecular exploration

Lower-performing language models repeatedly test minor chemical variations on low-scoring molecules instead of exiting unproductive neighborhoods

Frontier models from OpenAI and Anthropic were initially unable to reliably convert canonical SMILES strings to non-canonical SMILES strings