# Granite 4.2 3B vs LFM2.5-2.6B

> Granite 4.2 3B scores 59.8 (C) 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/granite-4-2-3b-vs-lfm2-5-2-6b
- Markdown: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.md (~6,100 tokens)
- Slim: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/granite-4-2-3b-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

Granite 4.2 3B scores 59.8 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 4 of 5 scored categories. Both do open-weight models you run yourself.

- Granite 4.2 3B: grade C, 59.8/100, rank #57 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.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

### Granite 4.2 3B (C)

Good for: Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off

Ahead on:
- Capability, 21 against 13
- Risk, 55 against 50
- Agent interface, 85 against 75
- Documentation and upkeep, 92 against 57

Also in its favour:
- Rated higher by the reviewer agents, 3.8 against 2.5 out of 5

Watch for: No safety results published for 4.2, and only general safety cautions

### 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

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


## Score by category

| Category | Weight | Granite 4.2 3B | LFM2.5-2.6B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 21 | 13 | Granite 4.2 3B +8 |
| Reach | 25% (25 this run) | 95 | 95 | even |
| Risk | 20% (20 this run) | 55 | 50 | Granite 4.2 3B +5 |
| Agent interface | 10% (10 this run) | 85 | 75 | Granite 4.2 3B +10 |
| Documentation and upkeep | 10% (10 this run) | 92 | 57 | Granite 4.2 3B +35 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.8 · C** | **51.5 · D** | |

## Facts side by side

| Fact | Granite 4.2 3B | LFM2.5-2.6B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | 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 | 2026-10-20 | 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

**Granite 4.2 3B.** A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.

**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

### Granite 4.2 3B

1. Start it with `ollama run granite4.2:3b`; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072
2. With vLLM 0.32.0 or later, serve `ibm-granite/granite-4.2-3b` with `--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice`; that release has the parser built in, so the card's `--reasoning-parser-plugin ./granite_thinking_parser.py` isn't needed
3. With SGLang 0.5.18 or later, `--reasoning-parser auto --tool-call-parser auto` resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content
4. Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires
5. Pass `enable_thinking` false for direct answers or `low_effort` true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server
6. Earlier turns' thinking is stripped by default (`truncate_history_thinking`), so pass it false to keep reasoning in multi-turn history
7. For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2
8. Verify the files with `python -m model_signing verify sigstore` and IBM's identity, per IBM's signature guide

### 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, Granite 4.2 3B or LFM2.5-2.6B?

Granite 4.2 3B scores 59.8 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 4 of 5 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "granite-4-2-3b", "b": "lfm2-5-2-6b"}`. From a terminal: `anchor compare granite-4-2-3b lfm2-5-2-6b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json and https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json

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