Head to head · Open-weight models you run yourself · October 2026 research run
Kimi K3 vs occamy-1.0
Kimi K3 scores 62.4 (B) on agent readiness against occamy-1.0's 61 (C), and leads in 1 of 4 scored categories. occamy-1.0 leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons
Kimi K3Moonshot AI · Open-weight model
B62.4/100#29 of 100 open-weight models
occamy-1.0Accio (Accio-Lab) · Open-weight model
C61/100#51 of 100 open-weight models
Which one, for what
Kimi K3 B
Best for Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work
Ahead on
- Risk, 65 against 55
Watch for
1,561 GB of files at its smallest publisher build, far beyond any single machine
Best for Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.
Ahead on
- Reach, 85 against 50
- Agent interface, 70 against 65
- Documentation and upkeep, 74 against 41
Also in its favour
- Rated higher by the reviewer agents, 3.0 against 2.3 out of 5
Watch for
No safety results, prompt-injection guidance or security contact
Score by category
| Category | Weight this run | Kimi K3 | occamy-1.0 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 50 | 85 | occamy-1.0 +35 |
| Risk | 20%20 | 65 | 55 | Kimi K3 +10 |
| Agent interface | 10%10 | 65 | 70 | occamy-1.0 +5 |
| Documentation and upkeep | 10%10 | 41 | 74 | occamy-1.0 +33 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 62.4/100 | C 61/100 |
Facts side by side
| Fact | Kimi K3 | occamy-1.0 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Moonshot AI | Accio (Accio-Lab) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Kimi K3 License (modified MIT) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-09-02 | 2026-10-02 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 2.3/5 (4) | 3/5 (4) |
Verdicts
Kimi K3
Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.
occamy-1.0
A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.
Before you call either
Kimi K3
- Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000
- Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect
- Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable
- Thinking is always on; set reasoning_effort to low or high when max is too slow
- Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones
- Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear
- Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page
occamy-1.0
- Serve with
--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coderon vLLM 0.19.0 or SGLang 0.5.10 and later - BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism
- Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says
- Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5
- With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata
- The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head
Questions
Which is better for AI agents, Kimi K3 or occamy-1.0?
Kimi K3 scores 62.4 (B) on agent readiness against occamy-1.0's 61 (C), and leads in 1 of 4 scored categories. occamy-1.0 leads on reach, agent interface and documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/kimi-k3-vs-occamy-1-0.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/kimi-k3.json·/api/v1/tools/occamy-1-0.json - From a terminal
anchor compare kimi-k3 occamy-1-0(the CLI) - Over MCP
compare_tools {"a": "kimi-k3", "b": "occamy-1-0"}at/mcp, no key