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

Kimi K3.1 Pro vs Nemotron 3 Super 120B A12B

Nemotron 3 Super 120B A12B scores 66.7 (B) 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 6359 open weights comparisons

Kimi K3.1 ProMoonshot AI · Open-weight model D48.6/100#151 of 160 open-weight models Nemotron 3 Super 120B A12BNVIDIA · Open-weight model B66.7/100#15 of 160 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

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

Nemotron 3 Super 120B A12B B

Best for Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data

Ahead on

  • Reach, 75 against 40
  • Risk, 65 against 50
  • Agent interface, 90 against 65
  • Documentation and upkeep, 70 against 51

Watch for

The card's serving commands pass --trust-remote-code

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 Nemotron 3 Super 120B A12B +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.

Nemotron 3 Super 120B A12B 75

Memory earns 10, because the smallest 4-bit build, NVIDIA's 80.3 GB NVFP4, needs about 96 GB with the fifth added, which fits the reference machine but not 64 GB, and Ollama's 87 GB build lands in the same band. Runtimes earn the full 25 from released Transformers, vLLM, SGLang, llama.cpp and Ollama. Builds earn 15 for NVIDIA's own FP8 and NVFP4. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it.

Agent interface Nemotron 3 Super 120B A12B +25

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.

Nemotron 3 Super 120B A12B 90

The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since NVIDIA documents the format, names the qwen3_coder parser for three runtimes and shows a tool call in its cookbook. Trained tool calling earns 20, with tool-use reinforcement learning and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off, low effort or a budget per request with the trace returned as reasoning_content. Context earns 10 for 262,144 tokens in the config and up to 1M per the card.

Documentation and upkeep Nemotron 3 Super 120B A12B +19

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.

Nemotron 3 Super 120B A12B 70

The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and token counts and the corpora published. Last change earns 20, counting NVIDIA's own inference code for the model, as on Nemotron 3 Ultra and Nano. The repositories last changed with a card edit on 25 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 0, with no NVIDIA reply in any of the nine discussions, re-counted on 1 November. Version policy earns 5 as a first release. File health earns 5, since a generation report waits without a reply and one card link is dead. 20+20+20+0+5+5 = 70.

Risk Nemotron 3 Super 120B A12B +15

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.

Nemotron 3 Super 120B A12B 65

File safety earns 15, safetensors only but with 10 off because the documented serving commands pass trust-remote-code and the repository ships model and parser code. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and says what safeguards to add, without an instruction hierarchy or concrete mitigations. Licence risk keeps all 15. The security programme earns 10 for the vulnerability submission route on the card and NVIDIA's public bulletins.

Score by category

CategoryWeight this runKimi K3.1 ProNemotron 3 Super 120B A12BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%254075Nemotron 3 Super 120B A12B +35
Risk20%205065Nemotron 3 Super 120B A12B +15
Agent interface10%106590Nemotron 3 Super 120B A12B +25
Documentation and upkeep10%105170Nemotron 3 Super 120B A12B +19
Negative events≤1500
TotalD 48.6/100B 66.7/100

Facts side by side

FactKimi K3.1 ProNemotron 3 Super 120B A12B
KindOpen-weight modelOpen-weight model
VendorMoonshot AINVIDIA
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceKimi K3.1 Pro License (modified MIT)NVIDIA Nemotron Open Model License
Read-only variant documentednono
llms.txtnono
Last release2026-11-052026-03-11
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone2.8/5 (4)

How the scores have moved

Kimi K3.1 Pro

No score change since it was listed.

Nemotron 3 Super 120B A12B

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.

Nemotron 3 Super 120B A12B

A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.

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

Nemotron 3 Super 120B A12B

  1. On the reference machine start it with ollama run nemotron-3-super, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin)
  2. Raise the Strix Halo GPU memory limit first with amd-ttm --set 100, since the default of about 62.8 GiB is far below what it needs
  3. With vLLM serve it with --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder (SGLang uses --reasoning-parser nemotron_3)
  4. Use temperature=1.0 and top_p=0.95 for every task, as the card says
  5. Thinking is on by default. Pass enable_thinking: false or low_effort: true in chat_template_kwargs for short tasks
  6. For coding agents pass force_nonempty_content: true in chat_template_kwargs, so a cut-off reasoning trace still returns content
  7. Raise context past 262,144 only with the card's flags (VLLM_ALLOW_LONG_MAX_MODEL_LEN=1, --max-model-len 1048576)

Questions

Which is better for AI agents, Kimi K3.1 Pro or Nemotron 3 Super 120B A12B?

Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category.

Other comparisons with Kimi K3.1 Pro or Nemotron 3 Super 120B A12B

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