Kimi K3.1 Pro is available through Moonshot’s apps and API. It is a useful model to compare with Claude Sonnet 5.5, Claude Opus 5.5, GPT-6 Astra, GLM-5.3 and DeepSeek V4.1 Flash-1017, but a single winner would hide important trade-offs. API token prices, independent benchmark scores, task-level token use and the right to host weights answer different questions.
Prices are only the starting point
The official Kimi API page lists $2 per million input tokens and $10 per million output tokens for K3.1 Pro. Anthropic lists the same standard input/output rates for Sonnet 5.5, with a lower cache-read rate. DeepSeek’s price page puts V4.1 Flash-1017 lower still, at $0.30/$1.20 during peak hours and half those rates off-peak. OpenAI lists GPT-6 Astra at $10/$50, with higher rates once a prompt exceeds 272,000 input tokens. These are standard API rates, not measured costs to finish a task.
Artificial Analysis’s current Kimi–Sonnet comparison illustrates the distinction. Its v5 evaluation scores Sonnet 54 and Kimi 46, yet estimates $10.02 per task for Sonnet at maximum effort and $3.68 for Kimi. Sonnet’s stronger measured capability and Kimi’s lower cost on that particular test can both be true. The result is a reason to benchmark your own work, not a guarantee that either will be cheaper or better for every request.
Do not mix index generations
At launch on October 23, Artificial Analysis reported Kimi at 55 and fourth on its then-current Intelligence Index. On October 27 it introduced v5, changing and adding evaluations. In v5, Kimi scores 46, while Opus 5.5 scores 58 and GPT-6 Astra 57 at maximum effort. The 55-point launch result is a valid historical snapshot, but it cannot be compared with v5 scores or advertised as today’s rank. The independent evaluator also reports DeepSeek V4.1 Flash-1017 and GLM-5.3 at 32 on v5, so neither lacks comparable evidence.
Moonshot reports a Kimi result close to Opus on Terminal-Bench 4.0 in its technical announcement. The vendor’s table combines results gathered under different harnesses and sources, so we use Artificial Analysis for cross-model conclusions. Moonshot separately describes an internal cyber test and an effect from removing certain training data. That finding does not explain a score gap on a different benchmark against another vendor.
Weights and licenses change the choice
Moonshot says it will release K3.1 Pro’s full weights by November 4, 2026. A date promised by a vendor is not a published weight file or a confirmed license. Kimi’s earlier K3 weights are available, but they are a different model. Teams needing downloadable weights now can inspect DeepSeek’s MIT-licensed files or GLM-5.3’s custom license. The GLM condition concerning a security review applies to Model-as-a-Service operators above a $10 billion aggregate-revenue threshold, not every large commercial user. Licensing and infrastructure suitability still require case-specific review.
For an API purchase, test real prompts across the models you can access, tally cached and uncached input plus output, and compare the quality of finished work. For self-hosting, check the actual model files and license on the day you deploy. This comparison records the evidence available on October 29, 2026.