AI model comparison
LFM2.5-2.6B vs gpt-oss-120b
Side by side comparison of LFM2.5-2.6B and gpt-oss-120b on pricing, benchmark scores, context window, and more.
LFM2.5-2.6B is a compact reasoning model from Liquid AI. It is suited for agent workflows, data extraction, RAG, and long-context processing.
- Input / 1M
- $0.35
- Output / 1M
- $0.50
- Context
- 128K
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases.
- Input / 1M
- $0.15
- Output / 1M
- $0.60
- Context
- 131K
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At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
gpt-oss-120b
1.6x cheaper for a typical chat
Higher intelligence score
gpt-oss-120b
11.6 vs 8.4 on Artificial Analysis
Larger context window
gpt-oss-120b
131K vs 128K tokens
Newer release
LFM2.5-2.6B
Released August 11, 2026
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
LFM2.5-2.6B8.4gpt-oss-120b11.6Coding index
LFM2.5-2.6B7.7gpt-oss-120b30.4Agentic index
LFM2.5-2.6BNot availablegpt-oss-120b3.7Pricing
LFM2.5-2.6B vs gpt-oss-120b API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | LFM2.5-2.6B | gpt-oss-120b |
|---|---|---|
| Input tokensPer 1M tokens | $0.35 | $0.15 |
| Output tokensPer 1M tokens | $0.50 | $0.60 |
| Cached input (read)Per 1M tokens | $0.04 | $0.08 |
| Cache writePer 1M tokens | Not available | Not available |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
LFM2.5-2.6B$0.95gpt-oss-120b$0.60Coding task
30K in, 4K out
LFM2.5-2.6B$12.50$3.20 cachedgpt-oss-120b$6.90$4.65 cachedLong document summary
150K in, 2K out
LFM2.5-2.6B$53.50$7 cachedgpt-oss-120b$23.70$12.45 cachedSpecs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | LFM2.5-2.6B | gpt-oss-120b |
|---|---|---|
| Context window | 128K tokens | 131K tokens |
| Max output | 8K tokens | 66K tokens |
| Input types | Text | Text |
| Output types | Text | Text |
| Reasoning effort levels | Not available | High, Medium, Low |
| Default reasoning effort | Not available | Medium |
| Release date | August 11, 2026 | August 5, 2025 |
Features
Supported features
API features available for each model.
| Metric | LFM2.5-2.6B | gpt-oss-120b |
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Supported | Supported |
| Temperature | Supported | Supported |
| Stop sequences | Supported | Supported |
| Deterministic seed | Supported | Supported |
| Verbosity control | Not supported | Not supported |
Keep exploring
More on these models
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Our take
gpt-oss-120b is 1.6x cheaper for a typical chat. gpt-oss-120b scores higher on the Artificial Analysis Intelligence Index (11.6 vs 8.4). gpt-oss-120b has the larger context window (131K vs 128K tokens). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is LFM2.5-2.6B or gpt-oss-120b cheaper?
gpt-oss-120b is cheaper for a typical chat (2K input tokens and 500 output tokens). gpt-oss-120b costs $0.15 per 1M input tokens and $0.60 per 1M output tokens, while LFM2.5-2.6B costs $0.35 per 1M input tokens and $0.50 per 1M output tokens.
Which has a bigger context window, LFM2.5-2.6B or gpt-oss-120b?
gpt-oss-120b supports up to 131K tokens of context, compared with 128K for LFM2.5-2.6B.
Is LFM2.5-2.6B smarter than gpt-oss-120b?
On the Artificial Analysis Intelligence Index, LFM2.5-2.6B scores 8.4 and gpt-oss-120b scores 11.6, putting gpt-oss-120b ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use LFM2.5-2.6B and gpt-oss-120b at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see LFM2.5-2.6B and gpt-oss-120b answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to LFM2.5-2.6B and gpt-oss-120b at the same time. Compare the answers side by side and keep the best one.