AI model comparison
LFM2.5-2.6B vs Llama 3.3 70B Instruct
Side by side comparison of LFM2.5-2.6B and Llama 3.3 70B Instruct 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
The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out).
- Input / 1M
- $0.10
- Output / 1M
- $0.32
- Context
- 131K
Comparing image generators? Compare image models
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
Llama 3.3 70B Instruct
2.6x cheaper for a typical chat
Higher intelligence score
LFM2.5-2.6B
8.4 vs 7.7 on Artificial Analysis
Larger context window
Llama 3.3 70B Instruct
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
LFM2.5-2.6B8.4Coding index
LFM2.5-2.6B7.7Agentic index
LFM2.5-2.6BNot availablePricing
LFM2.5-2.6B vs Llama 3.3 70B Instruct API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | LFM2.5-2.6B | |
|---|---|---|
| Input tokensPer 1M tokens | $0.35 | $0.10 |
| Output tokensPer 1M tokens | $0.50 | $0.32 |
| Cached input (read)Per 1M tokens | $0.04 | Not available |
| 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$0.95Coding task
30K in, 4K out
LFM2.5-2.6B$12.50$3.20 cachedLong document summary
150K in, 2K out
LFM2.5-2.6B$53.50$7 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 | |
|---|---|---|
| Context window | 128K tokens | 131K tokens |
| Max output | 8K tokens | 16K tokens |
| Input types | Text | Text |
| Output types | Text | Text |
| Reasoning effort levels | Not available | Not available |
| Default reasoning effort | Not available | Not available |
| Release date | August 11, 2026 | December 6, 2024 |
Features
Supported features
API features available for each model.
| Metric | LFM2.5-2.6B | |
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Supported | Not supported |
| Temperature | Supported | Supported |
| Stop sequences | Supported | Supported |
| Deterministic seed | Supported | Supported |
| Verbosity control | Not supported | Not supported |
Keep exploring
More on these models
Other head-to-heads, the full model page, and the alternatives list for each model.
- Llama 3.3 70B Instruct vs Muse Spark 1.3 Contributor
- Llama 3.3 70B Instruct vs Gemini 2.5 Flash Lite
- Llama 3.3 70B Instruct vs GPT-4o-mini
- Llama 3.3 70B Instruct vs Llama 3.1 8B Instruct
- Llama 3.3 70B Instruct vs Gemma 3 27B
- Llama 3.3 70B Instruct vs GPT-4.1 Nano
- Llama 3.3 70B Instruct vs Qwen3 30B A3B Instruct 2507
- Llama 3.3 70B Instruct vs Llama 4 Scout
- Llama 3.3 70B Instruct vs Llama 4 Maverick
- Llama 3.3 70B Instruct vs Qwen3 32B
- Llama 3.3 70B Instruct vs Nemotron 3 Nano 30B A3B
- Llama 3.3 70B Instruct vs Qwen3 14B
- Llama 3.3 70B Instruct vs Qwen2.5 72B Instruct
- Llama 3.3 70B Instruct vs Solar Pro 3
- Llama 3.3 70B Instruct vs Llama 3.1 70B Instruct
- Llama 3.3 70B Instruct vs Nova Lite 1.0
- Llama 3.3 70B Instruct vs Reka Flash 3
- Llama 3.3 70B Instruct vs Llama 3.2 3B Instruct
- Llama 3.3 70B Instruct vs Llama 3.2 1B Instruct
Our take
Llama 3.3 70B Instruct is 2.6x cheaper for a typical chat. LFM2.5-2.6B scores higher on the Artificial Analysis Intelligence Index (8.4 vs 7.7). Llama 3.3 70B Instruct 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 Llama 3.3 70B Instruct cheaper?
Llama 3.3 70B Instruct is cheaper for a typical chat (2K input tokens and 500 output tokens). Llama 3.3 70B Instruct costs $0.10 per 1M input tokens and $0.32 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 Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct supports up to 131K tokens of context, compared with 128K for LFM2.5-2.6B.
Is LFM2.5-2.6B smarter than Llama 3.3 70B Instruct?
On the Artificial Analysis Intelligence Index, LFM2.5-2.6B scores 8.4 and Llama 3.3 70B Instruct scores 7.7, putting LFM2.5-2.6B 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 Llama 3.3 70B Instruct at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see LFM2.5-2.6B and Llama 3.3 70B Instruct answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to LFM2.5-2.6B and Llama 3.3 70B Instruct at the same time. Compare the answers side by side and keep the best one.