Choose Inference.net: Schematron V2 Small if…
- you want the newer model — released 2026-09-12, 26 months after the other
Inference.net: Schematron V2 Small vs meta-llama/Llama-3.1-8B-Instruct
Neither wins outright: Inference.net: Schematron V2 Small is the pick when you want the newer model, meta-llama/Llama-3.1-8B-Instruct when cost matters at scale.
Key numbers
| Spec | Inference.net: Schematron V2 Small | meta-llama/Llama-3.1-8B-Instruct |
|---|---|---|
| Provider | Inference.net | Meta |
| Input price (per 1M tokens) | $0.05 | $0.05 |
| Output price (per 1M tokens) | $0.23 | $0.08 |
| Blended price (3:1 input/output) | $0.10 | $0.06 |
| Cheaper than … of all models we track | 82% | 86% |
| Context window | 128K tokens (~192 pages) | 131K tokens (~197 pages) |
| Larger context than … of all models | 14% | 22% |
| Max output per response | 4K tokens (~3,100 words) | 118K tokens (~88,500 words) |
| Input types | text | text |
| Output types | text | text |
| Free variant | No | No |
| Listed since | 2026-09-12 | 2024-07-23 |
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What it costs for real workloads
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) | Inference.net: Schematron V2 Small | meta-llama/Llama-3.1-8B-Instruct | Difference |
|---|---|---|---|
| Customer-support chatbot 50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens) | $8.35 | $5.35 | meta-llama/Llama-3.1-8B-Instruct saves $3.00 |
| RAG search over documents 20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens) | $8.30 | $6.80 | meta-llama/Llama-3.1-8B-Instruct saves $1.50 |
| 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) | $8.76 | $6.96 | meta-llama/Llama-3.1-8B-Instruct saves $1.80 |
| Batch summarisation 10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens) | $5.38 | $4.48 | meta-llama/Llama-3.1-8B-Instruct saves $0.90 |
| Long-document analysis 500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens) | $2.73 | $2.58 | meta-llama/Llama-3.1-8B-Instruct saves $0.15 |
Capabilities side by side
| Capability | Inference.net: Schematron V2 Small | meta-llama/Llama-3.1-8B-Instruct | What it lets you do |
|---|---|---|---|
| Tool / function calling | — | ✓ Yes | call your APIs and run agent loops |
| Structured output (JSON schema) | ✓ Yes | ✓ Yes | return JSON that validates against your schema |
| Reasoning / thinking mode | — | — | 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
- Inference.net: Schematron V2 Small: price unchanged since we started tracking it on 2026-09-12.
- meta-llama/Llama-3.1-8B-Instruct: input price raised 60% ($0.02 → $0.05/1M), output price raised 38% ($0.05 → $0.08/1M) on 2026-08-06.
| Date | Model | Input / 1M | Output / 1M |
|---|---|---|---|
| 2026-08-06 | meta-llama/Llama-3.1-8B-Instruct | $0.05 | $0.08 |
Which should you choose?
Choose meta-llama/Llama-3.1-8B-Instruct if…
- cost matters at scale — it is 39% cheaper on a typical 3:1 input/output mix ($5.35 vs $8.35 a month for 50,000 support chats)
- you need tool / function calling — to call your APIs and run agent loops; Inference.net: Schematron V2 Small does not advertise it
- you need log-probabilities — to read token confidence for classification and scoring; Inference.net: Schematron V2 Small does not advertise it
- you generate long outputs in one go — up to 118K tokens (about 88,500 words) per response
Frequently asked questions
Is Inference.net: Schematron V2 Small better than meta-llama/Llama-3.1-8B-Instruct?
Neither wins outright: Inference.net: Schematron V2 Small is the pick when you want the newer model, meta-llama/Llama-3.1-8B-Instruct when cost matters at scale. In short: Inference.net: Schematron V2 Small when you want the newer model; meta-llama/Llama-3.1-8B-Instruct when cost matters at scale, you need tool / function calling, you need log-probabilities, you generate long outputs in one go.
Which is cheaper, Inference.net: Schematron V2 Small or meta-llama/Llama-3.1-8B-Instruct?
meta-llama/Llama-3.1-8B-Instruct is cheaper: $0.06 vs $0.10 per 1M tokens at a typical 3:1 input/output mix, 39% less. For 50,000 support conversations a month that is $5.35 against $8.35.
How much do Inference.net: Schematron V2 Small and meta-llama/Llama-3.1-8B-Instruct cost per million tokens?
Inference.net: Schematron V2 Small costs $0.05 per 1M input tokens and $0.23 per 1M output tokens; meta-llama/Llama-3.1-8B-Instruct costs $0.05 input and $0.08 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, Inference.net: Schematron V2 Small or meta-llama/Llama-3.1-8B-Instruct?
meta-llama/Llama-3.1-8B-Instruct: 131K tokens, about 197 pages, against 128K (about 192 pages) for Inference.net: Schematron V2 Small.
Do Inference.net: Schematron V2 Small and meta-llama/Llama-3.1-8B-Instruct support function calling and JSON output?
Tool calling — Inference.net: Schematron V2 Small: not advertised, meta-llama/Llama-3.1-8B-Instruct: yes. JSON-schema structured output — Inference.net: Schematron V2 Small: yes, meta-llama/Llama-3.1-8B-Instruct: yes.
Can Inference.net: Schematron V2 Small or meta-llama/Llama-3.1-8B-Instruct read images?
Neither accepts image input — both are text-in models.
Is Inference.net: Schematron V2 Small or meta-llama/Llama-3.1-8B-Instruct free to use?
No free variant is listed for either; both are pay-per-token.
What is the maximum output length of Inference.net: Schematron V2 Small and meta-llama/Llama-3.1-8B-Instruct?
Inference.net: Schematron V2 Small can write up to 4K tokens (about 3,100 words) per response; meta-llama/Llama-3.1-8B-Instruct up to 118K (about 88,500 words).
When were Inference.net: Schematron V2 Small and meta-llama/Llama-3.1-8B-Instruct released?
Inference.net: Schematron V2 Small was listed on 2026-09-12; meta-llama/Llama-3.1-8B-Instruct on 2024-07-23.
Has the price of Inference.net: Schematron V2 Small or meta-llama/Llama-3.1-8B-Instruct changed?
Inference.net: Schematron V2 Small: price unchanged since we started tracking it on 2026-09-12. meta-llama/Llama-3.1-8B-Instruct: input price raised 60% ($0.02 → $0.05/1M), output price raised 38% ($0.05 → $0.08/1M) on 2026-08-06.
About the two models
Inference.net: Schematron V2 Small
Schematron V2 Small is a 3B-parameter HTML-to-JSON extraction model from Inference.net. It prioritizes extraction quality for complex schemas and long pages. Extraction instructions must be supplied through a JSON schema...
Full Inference.net: Schematron V2 Small specs →meta-llama/Llama-3.1-8B-Instruct
Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient. It has demonstrated strong performance compared to...
Full meta-llama/Llama-3.1-8B-Instruct specs →