Monthly cost at list price for five common workloads. Token counts per unit are stated so you can scale them to your own traffic. A workload that does not fit a model's context window is marked rather than priced.
Workload (per month)
LiquidAI: LFM2.5-2.6B
OpenAI: gpt-oss-safeguard-20b
Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$36.25
$11.63
OpenAI: gpt-oss-safeguard-20b saves $24.63
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$47.00
$12.00
OpenAI: gpt-oss-safeguard-20b saves $35.00
Coding / tool-using agent
2,000 tasks — each a multi-step task with tool calls and file context (60,000 in / 6,000 out tokens)
$48.00
$12.60
OpenAI: gpt-oss-safeguard-20b saves $35.40
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$31.00
$7.80
OpenAI: gpt-oss-safeguard-20b saves $23.20
Long-document analysis
500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)
$18.00
$4.05
OpenAI: gpt-oss-safeguard-20b saves $13.95
Capabilities side by side
Capability
LiquidAI: LFM2.5-2.6B
OpenAI: gpt-oss-safeguard-20b
What it lets you do
Tool / function calling
✓ Yes
✓ Yes
call your APIs and run agent loops
Structured output (JSON schema)
✓ Yes
✓ Yes
return JSON that validates against your schema
Reasoning / thinking mode
✓ Yes
✓ Yes
spend extra tokens thinking before answering hard problems
Image input
—
—
read screenshots, charts and scanned pages
Audio input
—
—
take speech or audio directly
File / PDF input
—
—
accept documents without your own parsing step
Image output
—
—
generate images, not just text
Built-in web search
—
—
answer from live web results
Seed (reproducible sampling)
✓ Yes
✓ Yes
repeat a generation for tests and evals
Log-probabilities
✓ Yes
—
read token confidence for classification and scoring
From each model's published parameters and input/output types. “—” means not advertised, not proven absent.
Price history
LiquidAI: LFM2.5-2.6B: price unchanged since we started tracking it on 2026-10-25.
OpenAI: gpt-oss-safeguard-20b: price unchanged since we started tracking it on 2026-03-10.
Which should you choose?
Choose LiquidAI: LFM2.5-2.6B if…
you need log-probabilities — to read token confidence for classification and scoring; OpenAI: gpt-oss-safeguard-20b does not advertise it
you want to prototype at zero cost — a free variant is listed
you want the newer model — released 2026-08-11, 10 months after the other
Choose OpenAI: gpt-oss-safeguard-20b if…
cost matters at scale — it is 66% cheaper on a typical 3:1 input/output mix ($11.63 vs $36.25 a month for 50,000 support chats)
you generate long outputs in one go — up to 66K tokens (about 49,200 words) per response
Frequently asked questions
Is LiquidAI: LFM2.5-2.6B better than OpenAI: gpt-oss-safeguard-20b?
Neither wins outright: LiquidAI: LFM2.5-2.6B is the pick when you need log-probabilities, OpenAI: gpt-oss-safeguard-20b when cost matters at scale. In short: LiquidAI: LFM2.5-2.6B when you need log-probabilities, you want to prototype at zero cost, you want the newer model; OpenAI: gpt-oss-safeguard-20b when cost matters at scale, you generate long outputs in one go.
Which is cheaper, LiquidAI: LFM2.5-2.6B or OpenAI: gpt-oss-safeguard-20b?
OpenAI: gpt-oss-safeguard-20b is cheaper: $0.13 vs $0.39 per 1M tokens at a typical 3:1 input/output mix, 66% less. For 50,000 support conversations a month that is $11.63 against $36.25.
How much do LiquidAI: LFM2.5-2.6B and OpenAI: gpt-oss-safeguard-20b cost per million tokens?
LiquidAI: LFM2.5-2.6B costs $0.35 per 1M input tokens and $0.50 per 1M output tokens; OpenAI: gpt-oss-safeguard-20b costs $0.07 input and $0.30 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, LiquidAI: LFM2.5-2.6B or OpenAI: gpt-oss-safeguard-20b?
OpenAI: gpt-oss-safeguard-20b: 131K tokens, about 197 pages, against 128K (about 192 pages) for LiquidAI: LFM2.5-2.6B.
Do LiquidAI: LFM2.5-2.6B and OpenAI: gpt-oss-safeguard-20b support function calling and JSON output?
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. Liquid advises against using it for agentic coding or...
gpt-oss-safeguard-20b is a safety reasoning model from OpenAI built upon gpt-oss-20b. This open-weight, 21B-parameter Mixture-of-Experts (MoE) model offers lower latency for safety tasks like content classification, LLM filtering, and trust...