Compare against the Sonnet setting you actually use. At its highest setting Sonnet 5.5 still scores 9 points more than Kimi K3.1 Pro, but each test task cost $10.02 against $3.68.
Which open-weight model can I use instead of Claude Sonnet without losing quality? It comes up when Sonnet runs in your product, the bill grows, and a customer asks where their data is processed.
Is there an open-source equivalent of Claude Sonnet?
There is no open-source version of Claude Sonnet. Claude Sonnet 5.5, released in September 2026, is a proprietary model, so you cannot download it or run it locally. The real question is which published model comes closest.
An open-weight model is one whose trained weights are published, so any provider can run the same model. If you change providers, the model and its behaviour stay the same. Kimi K3.1 Pro from Moonshot is one: it was released as an open-weights model in October 2026.
The gap to closed models is also smaller than it used to be. Since January 2026, the most capable open-weight models have trailed frontier closed models by an average of four months, according to Epoch AI. So the best open model today sits about where the best closed model was four months ago.
We are Lyceum, a sovereign European AI inference provider for open-source models. Our serverless models run in Paris and Finland, and you call them through one OpenAI-compatible API. We do not keep your prompts or outputs and never use them for training.
Open-weight models that score near Claude Sonnet 5.5
The Artificial Analysis Intelligence Index is a combined quality score built from 9 evaluations; its current version is v5.0.1. Sonnet 5.5 is a reasoning model, which thinks before it answers. Effort is how long it may think: low, medium, high, xhigh or max. More effort gives a higher score and costs more tokens.
Sonnet 5.5 scores 55 at max effort, 50 at xhigh, 42 at high and 31 at medium. Three of the open models we serve come closest.
| Model | Index score | Input | Cached input | Output |
|---|---|---|---|---|
| Kimi K3.1 Pro | 46 (max effort) | $2.00 | $0.20 | $10.00 |
| GLM-5.3 | 32 (max effort) | $1.40 | $0.26 | $4.40 |
| DeepSeek V4.1 Flash | 29 (high effort) | $0.50 | $0.13 | $1.50 |
Scores from Artificial Analysis, prices in US dollars per 1M tokens from our model pages, both read on 6 November 2026. For reference, Claude Sonnet 5.5 scores 55 (max), 50 (xhigh), 42 (high) and 31 (medium).
Kimi K3.1 Pro scores 46, which puts it between Sonnet 5.5 at high and xhigh effort.
GLM-5.3 scores 32 at max effort, close to Sonnet 5.5 at medium effort. DeepSeek V4.1 Flash scores 29 at high effort and is the cheapest of the three.
One combined score hides where a model is strong, as our guide on reading open-model benchmarks explains.
Coding and document work, test by test
Kimi K3.1 Pro scores higher than Sonnet 5.5 on terminal coding and level with it on document work. Both results come from Artificial Analysis, which runs every test itself.
Terminal-Bench 4.0 gives an agent 66 hard tasks to solve in a terminal. A task passes only if every one of its tests passes. Kimi K3.1 Pro scores 67.2%, the top score on the test. Claude Sonnet 5.5 at max effort scores 63.6%.
GDPval-AA v2.1 covers real-world tasks from 44 occupations, such as writing documents, slides and spreadsheets. Judges compare two answers blind, and the results become an Elo rating: a higher number means the model won more of those comparisons. Kimi K3.1 Pro rates 1858 and Sonnet 5.5 at max effort 1838, give or take 23. The two ranges overlap, so treat this as level. GLM-5.3 at max effort rates 1657.
We also sent the same two prompts to three of our models on 6 November 2026, at temperature 0 and with a cap of 16,000 output tokens. One asked for a short Python function that turns "1h30m" into seconds. The other asked for three fields from a German invoice, as JSON.
- Kimi K3.1 Pro returned a working function in all three calls, with 2,262 output tokens and 13.2 to 13.5 seconds each.
- GLM-5.3 returned a working function with 11,788 and 7,674 output tokens, in 41.6 and 25.0 seconds.
- DeepSeek V4.1 Flash hit the 16,000-token cap while still reasoning and returned no answer.
All three models got the invoice right in under a second. This is a smoke test, not a benchmark, but it shows what the scores do not: how many tokens a model spends before it answers. For extraction tasks, our guide on open models for document extraction goes further.
Where Claude Sonnet 5.5 still leads
Claude Sonnet 5.5 still leads on overall quality at its highest setting. At max effort it scores 55 on the index, 9 points above Kimi K3.1 Pro at 46.
It is also faster at its high setting. In Artificial Analysis's runs, Sonnet 5.5 at high effort took 7.0 minutes per index task. Kimi K3.1 Pro took 36.3 minutes, and Sonnet at max effort 31.5 minutes.
So if your product needs Sonnet's top quality on hard reasoning, none of the open models we serve has a published index score that high today. If you run Sonnet at high effort or below, Kimi K3.1 Pro scores higher, and GLM-5.3 scores about the same as medium effort.
What the same work costs
Kimi K3.1 Pro and Claude Sonnet 5.5 have the same list price per token: $2 per 1M input tokens and $10 per 1M output tokens. What differs is how many tokens each one writes for the same job.
Artificial Analysis publishes the average cost of one index task for every model. That figure already counts the tokens each model used. We use it for a hypothetical example: a team that runs 1,000 agent tasks a month like the ones in the index.
monthly cost = tasks per month x average cost per task
Kimi K3.1 Pro: 1,000 x $3.68 = $3,680| Model and setting | Index score | Cost per task | 1,000 tasks a month |
|---|---|---|---|
| Claude Sonnet 5.5, max effort | 55 | $10.02 | $10,020 |
| GLM-5.3, max effort | 32 | $5.01 | $5,010 |
| Kimi K3.1 Pro | 46 | $3.68 | $3,680 |
| Claude Sonnet 5.5, high effort | 42 | $1.64 | $1,640 |
Hypothetical workload, calculated from Artificial Analysis's average cost per index task, read on 6 November 2026.
The bill follows the tokens. On the index, Sonnet 5.5 generated 335M tokens at max effort and 54.7M at high effort. Kimi K3.1 Pro generated 113M. That is why Sonnet at high effort costs least, and Sonnet at max effort most.
GLM-5.3 shows the same effect. Its tokens are cheaper than Kimi K3.1 Pro's, yet one index task cost $5.01 against $3.68. Our coding test points the same way. At our list prices, Kimi K3.1 Pro's answer cost about $0.023, and GLM-5.3's cost $0.034 to $0.052.
The example does not show quality or cost on your own tasks. It leaves out caching, which bills repeated prompt text at the lower cached rate, and it says nothing about speed under load. For a single pair on price, see MiniMax M3 against Claude Sonnet 5.
How to check the match on your own tasks
Test Kimi K3.1 Pro against the Sonnet setting you run today, on your own tasks. Pick 1 to 3 candidates and a set of real requests with known good answers, including the hard ones.
If you work in Claude Code, our Anthropic-compatible endpoint answers its requests with our open models. Claude Code asks for a tier such as Sonnet. By default, our endpoint sends the Sonnet tier to DeepSeek V4.1 Flash, and the Fable tier to Kimi K3.1 Pro. To test Kimi K3.1 Pro in Sonnet's place, name it directly. The switch takes effect with your next message.
/model moonshotai/kimi-k3.1-proOur guide on open models in Claude Code covers the setup. From your own code, point an OpenAI client at our base URL and keep the rest.
from openai import OpenAI
client = OpenAI(base_url="https://api.lyceum.technology/openai/v1", api_key="lk_...")
resp = client.chat.completions.create(
model="moonshotai/kimi-k3.1-pro",
messages=[{"role": "user", "content": "Write a Python function that parses '1h30m' into seconds."}],
)
print(resp.choices[0].message.content, resp.usage.completion_tokens)For each request, check three things: whether the answer is right, how long it took, and how many output tokens it used. The token count is what you pay for. Our guide on testing an open model before you switch gives a fuller plan.
Switch when the open model gets your hard cases right at a cost per task you can carry. If you want help setting up the comparison, book a call to benchmark Kimi K3.1 Pro against Claude Sonnet on your own workload.
Frequently asked questions
Can I run Claude Sonnet locally?
No. Claude Sonnet is a proprietary model, and its weights are not published. To run a comparable model on your own terms, use an open-weight model such as Kimi K3.1 Pro.
Does Kimi K3.1 Pro read images and long documents?
Yes. On our API it accepts text and images, with a context window of up to 1M tokens.
Which model answers the Sonnet tier in Claude Code on Lyceum?
DeepSeek V4.1 Flash, by default. You can pick another of our models at any time by passing its model ID to the model command.