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

Kimi K3.1 Pro vs Ornith-1.5-397B

Ornith-1.5-397B scores 59 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 2 of 4 scored categories. Kimi K3.1 Pro leads on documentation and upkeep. 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

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

  • Documentation and upkeep, 51 against 36

Watch for

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

Ornith-1.5-397B C

Best for A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required

Ahead on

  • Reach, 65 against 40
  • Agent interface, 85 against 65

Watch for

Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine

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 Ornith-1.5-397B +25

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.

Ornith-1.5-397B 65

Memory 0, since the smallest 4-bit build, Ollama's at 242 GB, needs about 290 GB with the fifth added, above the 256 GB band, and Ornith's own Q4_K_M GGUF and NVFP4 builds need about the same. Runtimes 25, the most the line gives, from vLLM 0.19.1, SGLang 0.5.9, Transformers 5.8.1, llama.cpp's qwen35moe architecture and Ollama's library, while LM Studio's entry is the base's. Builds 15, Ornith publishes FP8, NVFP4 and GGUF at several precisions. Terms 15, MIT as named in the card's metadata on 21 October 2026, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it.

Agent interface Ornith-1.5-397B +20

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.

Ornith-1.5-397B 85

Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template turns thinking off per request with enable_thinking set to false and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.

Documentation and upkeep Kimi K3.1 Pro +15

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.

Ornith-1.5-397B 36

Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, Ornith uploaded new shards to the NVFP4 build on 20 October 2026. Responsiveness 0, nobody from Ornith answered either of the 2 questions, or any of the six on its GGUF and NVFP4 builds. Version policy 0, every GGUF file and the NVFP4 shards were replaced in place in August with no note. File health 0, the card links a missing LICENSE, the two chat templates differ, and the NVFP4 build carries shards its index doesn't use.

Score by category

CategoryWeight this runKimi K3.1 ProOrnith-1.5-397BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%254065Ornith-1.5-397B +25
Risk20%205050even
Agent interface10%106585Ornith-1.5-397B +20
Documentation and upkeep10%105136Kimi K3.1 Pro +15
Negative events≤1500
TotalD 48.6/100C 59/100

Facts side by side

FactKimi K3.1 ProOrnith-1.5-397B
KindOpen-weight modelOpen-weight model
VendorMoonshot AIOrnith
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceKimi K3.1 Pro License (modified MIT)MIT
Read-only variant documentednono
llms.txtnono
Last release2026-11-052026-08-23
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone2/5 (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.

Ornith-1.5-397B

Ornith's largest coding and terminal-agent model, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risks are a missing licence file, no safety results, no publisher replies and files replaced without a note.

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

Ornith-1.5-397B

  1. Start it with the card's vLLM command at --tensor-parallel-size 8, or ollama run ornith-1.5:397b on a machine with about 300 GB of memory
  2. In vLLM pass --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3, and in SGLang --tool-call-parser qwen3_coder --reasoning-parser qwen3, so tool calls and reasoning come back parsed
  3. Thinking is on by default. Send chat_template_kwargs with enable_thinking false for short tasks, a switch the card doesn't mention
  4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it
  5. When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards
  6. Pin a revision when you download, since Ornith has replaced files in place
  7. Validate JSON output yourself, since structured output isn't documented

Questions

Which is better for AI agents, Kimi K3.1 Pro or Ornith-1.5-397B?

Ornith-1.5-397B scores 59 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 2 of 4 scored categories. Kimi K3.1 Pro leads on documentation and upkeep.

Other comparisons with Kimi K3.1 Pro or Ornith-1.5-397B

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