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

Gemma 4 E4B IT vs Kimi K3.1 Pro

Gemma 4 E4B IT scores 63.2 (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 4969 open weights comparisons

Gemma 4 E4B ITGoogle DeepMind · Open-weight model B63.2/100#24 of 135 open-weight models Kimi K3.1 ProMoonshot AI · Open-weight model D48.6/100#130 of 135 open-weight models

Which one, for what

Gemma 4 E4B IT B

Best for Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning

Ahead on

  • Reach, 95 against 40
  • Risk, 70 against 50
  • Agent interface, 85 against 65
  • Documentation and upkeep, 75 against 51

Watch for

No safety evaluation figures published

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

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 Gemma 4 E4B IT +55

Gemma 4 E4B IT 95

Memory 30, Google's own 4-bit QAT GGUF with its projector is 6.15 GB, which with a fifth on top is 7.4 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build and two mobile builds, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one.

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 Gemma 4 E4B IT +24

Gemma 4 E4B IT 75

Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google's JAX library fixed the Gemma 4 audio embedding that this size runs on 5 October 2026, 28 days before this check, while the Hugging Face files last changed on 20 July. The line drops to 15 after 4 November unless the files or reference code change. Responsiveness 10, Google replied to 12 of 26 questions and reports, the last on 2026-07-17, with the five opened from 21 September to 1 November unanswered. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but reports about them wait without a reply. 20 + 10 + 20 + 10 + 10 + 5 = 75.

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.

Risk Gemma 4 E4B IT +20

Gemma 4 E4B IT 70

File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma advisories were found.

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.

Agent interface Gemma 4 E4B IT +20

Gemma 4 E4B IT 85

Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, and ships a response schema for Transformers' parser. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 131,072 tokens native, which meets the 128K line.

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.

Score by category

CategoryWeight this runGemma 4 E4B ITKimi K3.1 ProEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%259540Gemma 4 E4B IT +55
Risk20%207050Gemma 4 E4B IT +20
Agent interface10%108565Gemma 4 E4B IT +20
Documentation and upkeep10%107551Gemma 4 E4B IT +24
Negative events≤1500
TotalB 63.2/100D 48.6/100

Facts side by side

FactGemma 4 E4B ITKimi K3.1 Pro
KindOpen-weight modelOpen-weight model
VendorGoogle DeepMindMoonshot AI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Kimi K3.1 Pro License (modified MIT)
Read-only variant documentednono
llms.txtyesno
Last release2026-07-202026-11-05
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews3.5/5 (4)none

Verdicts

Gemma 4 E4B IT

A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 7 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.

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

Gemma 4 E4B IT

  1. On the reference machine run llama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072, and add --mmproj with gemma-4-E4B-it-mmproj.gguf for images and audio
  2. With Ollama run ollama run gemma4:e4b and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K
  3. In vLLM or SGLang pass --tool-call-parser gemma4 --reasoning-parser gemma4 (vLLM also --enable-auto-tool-choice), or tool calls come back as text; Google's w4a16 build is the 4-bit route there
  4. Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
  5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them

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

Questions

Which is better for AI agents, Gemma 4 E4B IT or Kimi K3.1 Pro?

Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in every scored category.

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