Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 3B vs Kimi K3.1 Pro
Granite 4.2 3B scores 59.8 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category. 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 on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off
Ahead on
- Reach, 95 against 40
- Risk, 55 against 50
- Agent interface, 85 against 65
- Documentation and upkeep, 92 against 51
Watch for
No safety results published for 4.2, and only general safety cautions
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.
Reach Granite 4.2 3B +55
Granite 4.2 3B 95
Memory earns the full 30, since IBM's 4-bit MLX build is 2.07 GB and Ollama's Q4_K_M build 2.2 GB, about 2.5 to 2.6 GB with the fifth added. Five named runtimes run the granite architecture in released versions or their own libraries, so runtimes take the cap of 25, though LM Studio has no 4.2 entry. IBM's FP8, FP4, GGUF and MLX builds earn 15. Apache 2.0 with nothing added gives terms 15 and the ungated repositories download 10. Hosted too is 0, since only DeepInfra serves it and watsonx.ai stops at 4.1.
Kimi K3.1 Pro 40
Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40.
Documentation and upkeep Granite 4.2 3B +41
Granite 4.2 3B 92
The card covers everything but a knowledge cut-off (17). IBM's disclosure lists the datasets with sources and sizes (20). The index file changed on 20 October 2026, 15 days before this check, and nothing in IBM's 4.2 repositories or the GitHub repository changed since (20). The two questions asked on IBM's 3B repositories have an IBM reply, and no new thread was opened after 21 October (20). Granite 4.1 3B stays in its own repository with no note (5), and the files agree with each other (10).
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.
Agent interface Granite 4.2 3B +20
Granite 4.2 3B 85
The template ships in the repository (20). The card shows how tools are declared and how calls come back, and names qwen3_coder for vLLM and SGLang (25). The template renders several calls in a turn (20). JSON output isn't documented for 4.2 (0). Thinking turns off or goes to low effort per request, with the reasoning returned apart in SGLang (10), and 131,072 tokens are native (10).
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.
Risk Granite 4.2 3B +5
Granite 4.2 3B 55
File safety gets 15 of 25, safetensors only, less 10 because the card's vLLM command loads the repository's parser plugin. Provenance gets 15 for files in IBM's own organisation with hashes and a sigstore signature. Safety evaluation is 0, since 4.2 publishes no safety results. Agent guidance gets 5 for general cautions and the advice to add Granite Guardian. Apache 2.0 keeps licence risk at 15, and IBM's security.txt gives 5 of the security programme's 10.
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.
Score by category
| Category | Weight this run | Granite 4.2 3B | Kimi K3.1 Pro | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 40 | Granite 4.2 3B +55 |
| Risk | 20%20 | 55 | 50 | Granite 4.2 3B +5 |
| Agent interface | 10%10 | 85 | 65 | Granite 4.2 3B +20 |
| Documentation and upkeep | 10%10 | 92 | 51 | Granite 4.2 3B +41 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 59.8/100 | D 48.6/100 |
Facts side by side
| Fact | Granite 4.2 3B | Kimi K3.1 Pro |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | Moonshot AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Kimi K3.1 Pro License (modified MIT) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-20 | 2026-11-05 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3.8/5 (4) | none |
Verdicts
Granite 4.2 3B
A small reasoning model for tool calls, coding and multilingual chat, published by IBM under plain Apache 2.0 with signed files and a dataset disclosure, and run by llama.cpp, Ollama, vLLM, SGLang and Transformers from a 2.2 GB Ollama build. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.
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
Granite 4.2 3B
- Start it with
ollama run granite4.2:3b; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072 - With vLLM 0.32.0 or later, serve
ibm-granite/granite-4.2-3bwith--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice; that release has the parser built in, so the card's--reasoning-parser-plugin ./granite_thinking_parser.pyisn't needed - With SGLang 0.5.18 or later,
--reasoning-parser auto --tool-call-parser autoresolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content - Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires
- Pass
enable_thinkingfalse for direct answers orlow_efforttrue for short reasoning, through chat_template_kwargs on an OpenAI-compatible server - Earlier turns' thinking is stripped by default (
truncate_history_thinking), so pass it false to keep reasoning in multi-turn history - For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2
- Verify the files with
python -m model_signing verify sigstoreand IBM's identity, per IBM's signature guide
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, Granite 4.2 3B or Kimi K3.1 Pro?
Granite 4.2 3B scores 59.8 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category.
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Machine-readable
- This page as Markdown
/compare/granite-4-2-3b-vs-kimi-k3-1-pro.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/granite-4-2-3b.json·/api/v1/tools/kimi-k3-1-pro.json - From a terminal
anchor compare granite-4-2-3b kimi-k3-1-pro(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-3b", "b": "kimi-k3-1-pro"}at/mcp, no key