Decision support

LFM3-1.7B vs Ternary Bonsai 2 9B

Compare canonical objects directly. The useful dimensions change with the object type, so model and hardware comparisons stay separate.

LFM3-1.7B

Liquid AI

Sources:OpenRouter

Benchmark results

No benchmark results have been matched to these models yet.

Model specifications

Release date
November 6, 2026
October 16, 2026
Context window
262K
262K
Native context
256K
—
Max output tokens
8,192
65,536
Knowledge cutoff
—
—
Open weights
Open weights
Open weights
Parameter count
1.7B
9B

API pricing · USD per 1M tokens

Input
$0.08
$0.08
Output
$0.20
$0.13
Cached input
$0.02
$0.04
Cache write
—
—

Capabilities and modalities

Input modalities
text
text, image
Output modalities
text
text
Tool calling
Supported
Supported
Reasoning option
Supported
Supported
Supported options
frequency_penalty, include_reasoning, logprobs, max_completion_tokens, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
frequency_penalty, include_reasoning, logprobs, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
Capabilities
—
—

Architecture and availability

Architecture
—
—
Model families
—
—
Tokenizer
Other
Qwen
Instruction format
—
—
License
—
—
Providers
1
1
Provider endpoints
1
1
Ollama variants
0
0
Metadata source
ai-research
openrouter

A dash means the field is not reported. Provider-specific performance and pricing may differ.

To give you the best experience, we use cookies for personalization, insights, and performance. Learn more.