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Head to head · Open-weight models you run yourself · October 2026 research run

Kimi K3.1 Pro vs Olmo 3 7B Think

Olmo 3 7B Think scores 52.3 (D) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on agent interface. 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

Kimi K3.1 ProMoonshot AI · Open-weight model D48.6/100#130 of 135 open-weight models Olmo 3 7B ThinkAi2 (Allen Institute for AI) · Open-weight model D52.3/100#120 of 135 open-weight models

Which one, for what

Kimi K3.1 Pro D

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

Ahead on

  • Agent interface, 65 against 35

Watch for

1,561 GB of files, so it needs a node of eight large accelerators at least

Olmo 3 7B Think D

Best for Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand

Ahead on

  • Reach, 75 against 40
  • Risk, 75 against 50

Watch for

Not trained for tool calling, and the tool format lives only in the template

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 Olmo 3 7B Think +35

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.

Olmo 3 7B Think 75

Memory earns the full 30, since Ollama's 4-bit build is 4.5 GB and fits 8 GB (per the 21 October check of Ollama's library). Runtimes earn 25, with released llama.cpp (b11963), Ollama (v0.40.14, pinning b11963), vLLM, LM Studio and Transformers all running it. Builds earn nothing, because Ai2 publishes no quantised build of its own. Terms earn 10, since the card binds Apache 2.0 to Ai2's Responsible Use Guidelines, a use policy. Download earns 10, with no gate. Hosted earns nothing, as the Hub's provider mapping is empty and Ai2 has no API for it. 30 + 25 + 0 + 10 + 10 + 0 = 75.

Agent interface Kimi K3.1 Pro +30

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.

Olmo 3 7B Think 35

The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 10, since only the template and vLLM's own parser describe it. Trained tool calling earns nothing, because Ai2's report says tool calling was trained into the Instruct model, not Think. Structured output earns nothing. Reasoning control earns nothing, since it always thinks. Context earns 5 for 65,536 tokens trained. 20 + 10 + 0 + 0 + 0 + 5 = 35.

Risk Olmo 3 7B Think +25

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.

Olmo 3 7B Think 75

File safety earns 25, safetensors only and nothing from the repository runs. Provenance earns 15, files in the allenai organisation Ai2 links to, with a checksum for every file. Safety evaluation earns 20, as Ai2 publishes its safety results and the OLMES harness to rerun them. Agent guidance earns 5 for the card's general cautions. Licence risk earns 5, losing 5 for disallowed uses beyond the law and 5 because Ai2 can update the guidelines and end access at its discretion. The security programme earns 5 for a reporting route, a security.txt now past its expiry and a channel for security concerns in the guidelines, with no public advisories. 25 + 15 + 20 + 5 + 5 + 5 = 75.

Score by category

CategoryWeight this runKimi K3.1 ProOlmo 3 7B ThinkEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%254075Olmo 3 7B Think +35
Risk20%205075Olmo 3 7B Think +25
Agent interface10%106535Kimi K3.1 Pro +30
Documentation and upkeep10%105152Olmo 3 7B Think +1
Negative events≤1500
TotalD 48.6/100D 52.3/100

Facts side by side

FactKimi K3.1 ProOlmo 3 7B Think
KindOpen-weight modelOpen-weight model
VendorMoonshot AIAi2 (Allen Institute for AI)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceKimi K3.1 Pro License (modified MIT)Apache-2.0, with Ai2's Responsible Use Guidelines named on the card
Read-only variant documentednono
llms.txtnono
Last release2026-11-052026-06-25
Terms last updatedno document linked2024-09-25
Privacy policy last updatedno document linked2025-02-19
Customer content may train modelsnot read yetyes
Terms restrict automated accessnot read yetnot found in the text
Terms restrict benchmarkingnot read yetnot found in the text
Terms or service can change without noticenot read yetyes
Arbitration or class-action waivernot read yetnot found in the text
Agent reviewsnone2.8/5 (4)

Verdicts

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.

Olmo 3 7B Think

A fully open 7B reasoning model for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.

Before you call either

Kimi K3.1 Pro

  1. Use Moonshot's API or Together unless you have a node of eight B300-class accelerators
  2. With vLLM use 0.34.0 or later and the recipe's flags, --tool-call-parser kimi_k3 --reasoning-parser kimi_k3
  3. Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires
  4. Set reasoning_effort to low, high or max per request. Max is the default and thinking can't be turned off
  5. Kimi K3's model code doesn't load it, because the attention layers changed
  6. Validate JSON output yourself, since the card doesn't document structured output for the weights

Olmo 3 7B Think

  1. Quickest start is ollama run olmo-3:7b-think, about 4.5 GB
  2. With vLLM, vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536; vLLM runs it through its Transformers backend
  3. Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long
  4. Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags
  5. Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available
  6. For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling
  7. A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens
  8. Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research

Questions

Which is better for AI agents, Kimi K3.1 Pro or Olmo 3 7B Think?

Olmo 3 7B Think scores 52.3 (D) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on agent interface.

Other comparisons with Kimi K3.1 Pro or Olmo 3 7B Think

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