# Gemma 4 26B A4B IT vs LFM2.5-2.6B

> Gemma 4 26B A4B IT scores 72.2 (BB) to LFM2.5-2.6B's 51.5 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b
- Markdown: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.md (~6,200 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 4 of 5 scored categories. LFM2.5-2.6B leads on reach. Both do open-weight models you run yourself.

- Gemma 4 26B A4B IT: grade BB, 72.2/100, rank #4 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json
- LFM2.5-2.6B: grade D, 51.5/100, rank #89 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm2-5-2-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Gemma 4 26B A4B IT (BB)

Good for: Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context

Ahead on:
- Capability, 59 against 13
- Risk, 70 against 50
- Agent interface, 85 against 75
- Documentation and upkeep, 65 against 57

Also in its favour:
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 2.5 out of 5

Watch for: No safety evaluation figures published

### LFM2.5-2.6B (D)

Good for: Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold

Ahead on:
- Reach, 95 against 90

Watch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue


## Score by category

| Category | Weight | Gemma 4 26B A4B IT | LFM2.5-2.6B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 59 | 13 | Gemma 4 26B A4B IT +46 |
| Reach | 25% (25 this run) | 90 | 95 | LFM2.5-2.6B +5 |
| Risk | 20% (20 this run) | 70 | 50 | Gemma 4 26B A4B IT +20 |
| Agent interface | 10% (10 this run) | 85 | 75 | Gemma 4 26B A4B IT +10 |
| Documentation and upkeep | 10% (10 this run) | 65 | 57 | Gemma 4 26B A4B IT +8 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **72.2 · BB** | **51.5 · D** | |

## Facts side by side

| Fact | Gemma 4 26B A4B IT | LFM2.5-2.6B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Liquid AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | LFM Open License v1.0 (free commercial use below $10M annual revenue) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-07-20 | 2026-09-22 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Agent reviews | 3.8/5 (4) | 2.5/5 (4) |

## Verdicts

**Gemma 4 26B A4B IT.** A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.

**LFM2.5-2.6B.** A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.

## Before you call either

### Gemma 4 26B A4B IT

1. On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images
2. With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM 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
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. Validate tool names and arguments before running them, and check any JSON yourself

### LFM2.5-2.6B

1. Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`
2. With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately
3. Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set
4. Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead
5. Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought
6. Earlier thinking is dropped from the prompt unless `preserve_thinking` is true
7. Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid
8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, Gemma 4 26B A4B IT or LFM2.5-2.6B?

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 4 of 5 scored categories. LFM2.5-2.6B leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-26b-a4b-it", "b": "lfm2-5-2-6b"}`. From a terminal: `anchor compare gemma-4-26b-a4b-it lfm2-5-2-6b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json and https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json

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