# gpt-oss-120b vs LFM2.5-2.6B

> gpt-oss-120b scores 70.2 (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/gpt-oss-120b-vs-lfm2-5-2-6b
- Markdown: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.md (~6,000 tokens)
- Slim: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gpt-oss-120b-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

gpt-oss-120b scores 70.2 (BB) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 3 of 5 scored categories. LFM2.5-2.6B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.

- gpt-oss-120b: grade BB, 70.2/100, rank #6 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gpt-oss-120b.md · JSON https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.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

### gpt-oss-120b (BB)

Good for: Agents on one 80 GB GPU or a 128 GB unified machine that want an Apache 2.0 reasoning model with wide runtime support

Ahead on:
- Capability, 54 against 13
- Risk, 95 against 50
- Agent interface, 90 against 75

Also in its favour:
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.5 against 2.5 out of 5

Watch for: Needs about 78 GB, too big for 64 GB machines, and OpenAI ships only one precision

### 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 75
- Documentation and upkeep, 57 against 45

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


## Score by category

| Category | Weight | gpt-oss-120b | LFM2.5-2.6B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 54 | 13 | gpt-oss-120b +41 |
| Reach | 25% (25 this run) | 75 | 95 | LFM2.5-2.6B +20 |
| Risk | 20% (20 this run) | 95 | 50 | gpt-oss-120b +45 |
| Agent interface | 10% (10 this run) | 90 | 75 | gpt-oss-120b +15 |
| Documentation and upkeep | 10% (10 this run) | 45 | 57 | LFM2.5-2.6B +12 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **70.2 · BB** | **51.5 · D** | |

## Facts side by side

| Fact | gpt-oss-120b | LFM2.5-2.6B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenAI | 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 | 2025-08-26 | 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 | 3.5/5 (4) | 2.5/5 (4) |

## Verdicts

**gpt-oss-120b.** A reasoning and agent cousin that runs in most runtimes under Apache 2.0 with published safety work. It needs about 78 GB of GPU memory, one 80 GB card or a 128 GB unified machine with the GPU limit raised. The main risks are tool calls one at a time and no replies to reports since August 2025.

**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

### gpt-oss-120b

1. Start it with `ollama run gpt-oss:120b` on the reference machine, the build that fits
2. On the Strix Halo machine raise the GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is below the 78 GB it needs
3. With vLLM use `vllm serve openai/gpt-oss-120b --tool-call-parser openai --enable-auto-tool-choice` per vLLM's recipe, or parse raw output with openai-harmony
4. Always use the harmony format through the chat template or openai-harmony, the card says it won't work correctly otherwise
5. Set `Reasoning: low` in the system message for short tasks, medium is the default
6. Pass the reasoning back after a tool call until the final answer, and keep the analysis channel away from end users
7. Expect one tool call per turn and loop, don't wait for parallel calls

### 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, gpt-oss-120b or LFM2.5-2.6B?

gpt-oss-120b scores 70.2 (BB) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 3 of 5 scored categories. LFM2.5-2.6B leads on reach and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gpt-oss-120b", "b": "lfm2-5-2-6b"}`. From a terminal: `anchor compare gpt-oss-120b lfm2-5-2-6b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gpt-oss-120b.json and https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json

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