Neither wins outright: AI21: Jamba Large 1.7 is the pick when you need to fit long inputs in one request, Inference.net: Schematron V2 Turbo when cost matters at scale.
| Spec | AI21: Jamba Large 1.7 | Inference.net: Schematron V2 Turbo |
|---|---|---|
| Provider | AI21 | Inference.net |
| Input price (per 1M tokens) | $2.00 | $0.03 |
| Output price (per 1M tokens) | $8.00 | $0.15 |
| Blended price (3:1 input/output) | $3.50 | $0.06 |
| Cheaper than … of all models we track | 18% | 86% |
| Context window | 256K tokens (~384 pages) | 128K tokens (~192 pages) |
| Larger context than … of all models | 44% | 14% |
| Max output per response | 4K tokens (~3,100 words) | 8K tokens (~6,100 words) |
| Input types | text | text |
| Output types | text | text |
| Free variant | No | No |
| Listed since | 2025-08-08 | 2026-09-12 |
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) | AI21: Jamba Large 1.7 | Inference.net: Schematron V2 Turbo | Difference |
|---|---|---|---|
| Customer-support chatbot 50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens) | $310 | $5.25 | Inference.net: Schematron V2 Turbo saves $305 |
| RAG search over documents 20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens) | $320 | $5.10 | Inference.net: Schematron V2 Turbo saves $315 |
| 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) | $336 | $5.40 | Inference.net: Schematron V2 Turbo saves $331 |
| Batch summarisation 10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens) | $208 | $3.30 | Inference.net: Schematron V2 Turbo saves $205 |
| Long-document analysis 500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens) | $108 | $1.65 | Inference.net: Schematron V2 Turbo saves $106 |
| Capability | AI21: Jamba Large 1.7 | Inference.net: Schematron V2 Turbo | 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 | repeat a generation for tests and evals |
| Log-probabilities | — | — | read token confidence for classification and scoring |
From each model's published parameters and input/output types. “—” means not advertised, not proven absent.
Neither wins outright: AI21: Jamba Large 1.7 is the pick when you need to fit long inputs in one request, Inference.net: Schematron V2 Turbo when cost matters at scale. In short: AI21: Jamba Large 1.7 when you need to fit long inputs in one request, you need tool / function calling; Inference.net: Schematron V2 Turbo when cost matters at scale, you need seed (reproducible sampling), you generate long outputs in one go, you want the newer model.
Inference.net: Schematron V2 Turbo is cheaper: $0.06 vs $3.50 per 1M tokens at a typical 3:1 input/output mix, 98% less. For 50,000 support conversations a month that is $5.25 against $310.
AI21: Jamba Large 1.7 costs $2.00 per 1M input tokens and $8.00 per 1M output tokens; Inference.net: Schematron V2 Turbo costs $0.03 input and $0.15 output. Output tokens are billed separately, so long answers shift the gap.
AI21: Jamba Large 1.7: 256K tokens, about 384 pages, against 128K (about 192 pages) for Inference.net: Schematron V2 Turbo.
Tool calling — AI21: Jamba Large 1.7: yes, Inference.net: Schematron V2 Turbo: not advertised. JSON-schema structured output — AI21: Jamba Large 1.7: yes, Inference.net: Schematron V2 Turbo: yes.
Neither accepts image input — both are text-in models.
No free variant is listed for either; both are pay-per-token.
AI21: Jamba Large 1.7 can write up to 4K tokens (about 3,100 words) per response; Inference.net: Schematron V2 Turbo up to 8K (about 6,100 words).
AI21: Jamba Large 1.7 was listed on 2025-08-08; Inference.net: Schematron V2 Turbo on 2026-09-12.
Jamba Large 1.7 is the latest model in the Jamba open family, offering improvements in grounding, instruction-following, and overall efficiency. Built on a hybrid SSM-Transformer architecture with a 256K context...
Full AI21: Jamba Large 1.7 specs →Schematron V2 Turbo is a 3B-parameter HTML-to-JSON extraction model from Inference.net. It prioritizes throughput for high-volume extraction workloads. Extraction instructions must be supplied through a JSON schema in response_format rather...
Full Inference.net: Schematron V2 Turbo specs →