LFM2.5-2.6B vs Llama 3.1 70B Instruct
Prices checked
LFM2.5-2.6B takes 3 of 5 rounds against Llama 3.1 70B Instruct. It is also the cheaper of the two to run, at $0.13 per million tokens against $0.17.
LFM2.5-2.6B
128k context · tool calling · open weights
$0.13/M tokensCheapest price, at NanoGPT · 7.2¢ per 1,000 pages
Llama 3.1 70B Instruct
131k context · tool calling · open weights
$0.17/M tokensCheapest price, at Hyperbolic · 9¢ per 1,000 pages
Maker's price in (lower wins)
$0.35/M
$0.40/M
Maker's price out (lower wins)
$0.50/M
$0.40/M
Cheapest price (lower wins)
$0.13/M at NanoGPT
$0.17/M at Hyperbolic
Maker's price, blended (lower wins)
$0.39/M
$0.40/M
Cost per 1,000 pages (lower wins)
7.2¢
9¢
Context
128k tokens
131k tokens
Open weights
yes
yes
Tool calling
yes
yes
Structured output
yes
yes
Reads images
no
no
Reasoning
yes
no
Released
11 Aug 2026
23 Jul 2024
Providers selling it
4
18
Price we measure against
the maker's own
OpenRouter
Trains on your prompts (no is better)
not published
not published
Providers selling both · 2
Kilo Gateway$0.39 / $0.40
OpenRouter$0.39 / $0.40
LFM2.5-2.6B, then Llama 3.1 70B Instruct, per million tokens.
Go further
Add a third or fourth model, or change the weighting, in the tool.
What moved this week
Eden AI · 100% dearer · $0.80 / $0.80 $0.40 / $0.40
Frequently asked
- Which is cheaper, LFM2.5-2.6B or Llama 3.1 70B Instruct?
- LFM2.5-2.6B, at $0.13 per million tokens against $0.17 for Llama 3.1 70B Instruct, blended 3 in : 1 out, read on 25 Oct 2026.
- Where is each one cheapest?
- LFM2.5-2.6B: $0.13 at NanoGPT. Llama 3.1 70B Instruct: $0.17 at Hyperbolic.
- Can I buy both from one provider?
- Yes. 2 providers sell both: Kilo Gateway, OpenRouter. The cheapest of them for the two together is Kilo Gateway.
- Which holds more context?
- Llama 3.1 70B Instruct, at 131k tokens against 128k.
Prices exclude tax, read 25 Oct 2026 from each provider's own catalog. They are per M tokens, blended 3 in : 1 out, and a page is 600 tokens in and 60 out. Bars are scaled to the larger value in each row.
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