Head to head · Open-weight models you run yourself · October 2026 research run
Apertus v1.5 8B vs Kimi K3.1 Pro
Apertus v1.5 8B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons
Which one, for what
Best for Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.
Ahead on
- Risk, 75 against 50
- Agent interface, 85 against 65
- Documentation and upkeep, 84 against 51
Watch for
No released runtime supports it, only Swiss AI's forks of vLLM and Transformers
Best for Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
1,561 GB of files, so it needs a node of eight large accelerators at least
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Documentation and upkeep Apertus v1.5 8B +33
Apertus v1.5 8B 84
Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 20, every dataset is public and listed with versions, licences and counts. Last change 20, the card changed on 14 October 2026, 20 days before 3 November, and Swiss AI added its own evaluation-results files on 26 October and 3 November. Responsiveness 20, Swiss AI answered all three questions, unchanged on 3 November 2026. Version policy 5, earlier models stay in their own repositories without a dated note. File health 5, the gated config couldn't be checked against the card, and the card and the policy disagree on the output filter.
Kimi K3.1 Pro 51
Model card 11, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51.
Risk Apertus v1.5 8B +25
Apertus v1.5 8B 75
File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the swiss-ai organisation the report links, with a checksum on every file. Safety evaluation 10 of 20, published safety benchmarks run with Swiss AI's open harnesses and early access for three outside evaluators, but the manual evaluation is Swiss AI's own and can't be rerun. Agent guidance 10, the report says what the refusals cover, how far jailbreaks get and that capability limits are the main defence, with a personal-data filter to apply, but nothing on prompt injection for deployers. Licence risk 10, 5 off for the use policy. Security programme 5, ETH Zurich's disclosure policy, with no advisories found.
Kimi K3.1 Pro 50
File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50.
Agent interface Apertus v1.5 8B +20
Apertus v1.5 8B 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, Swiss AI's report documents how tools are declared and returned and the card names vLLM's apertus parser. Trained tool calling 20, trained on verified tool use with several calls a turn, though parallel calls are weaker on the 8B and tools don't work with thinking on. Structured output 0, not documented. Reasoning control 10, thinking turns on per request and the card says where the reasoning comes back. Context 10, 262,144 tokens trained.
Kimi K3.1 Pro 65
Chat template 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65.
Score by category
| Category | Weight this run | Apertus v1.5 8B | Kimi K3.1 Pro | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 40 | 40 | even |
| Risk | 20%20 | 75 | 50 | Apertus v1.5 8B +25 |
| Agent interface | 10%10 | 85 | 65 | Apertus v1.5 8B +20 |
| Documentation and upkeep | 10%10 | 84 | 51 | Apertus v1.5 8B +33 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | D 48.6/100 |
Facts side by side
| Fact | Apertus v1.5 8B | Kimi K3.1 Pro |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | Moonshot AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate | Kimi K3.1 Pro License (modified MIT) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-14 | 2026-11-05 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.8/5 (4) | none |
Verdicts
Apertus v1.5 8B
A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.
Kimi K3.1 Pro
Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet.
Before you call either
Apertus v1.5 8B
- Accept the terms on the card and set HF_TOKEN before downloading
- Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet
- Serve with
--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertusfor tools, and leave thinking off when you need tools - For thinking, set
--reasoning-parser apertusand--default-chat-template-kwargs.enable_thinking true, and budget max tokens generously - Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames
- Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32
- If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent
Kimi K3.1 Pro
- Use Moonshot's API or Together unless you have a node of eight B300-class accelerators
- With vLLM use 0.34.0 or later and the recipe's flags,
--tool-call-parser kimi_k3 --reasoning-parser kimi_k3 - Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires
- Set
reasoning_effortto low, high or max per request. Max is the default and thinking can't be turned off - Kimi K3's model code doesn't load it, because the attention layers changed
- Validate JSON output yourself, since the card doesn't document structured output for the weights
Questions
Which is better for AI agents, Apertus v1.5 8B or Kimi K3.1 Pro?
Apertus v1.5 8B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories.
Other comparisons with Apertus v1.5 8B or Kimi K3.1 Pro
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Machine-readable
- This page as Markdown
/compare/apertus-v1-5-8b-vs-kimi-k3-1-pro.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/apertus-v1-5-8b.json·/api/v1/tools/kimi-k3-1-pro.json - From a terminal
anchor compare apertus-v1-5-8b kimi-k3-1-pro(the CLI) - Over MCP
compare_tools {"a": "apertus-v1-5-8b", "b": "kimi-k3-1-pro"}at/mcp, no key