Neither wins outright: DeepSeek: DeepSeek V3.1 Terminus (exacto) is the pick when you need tool / function calling, Inference.net: Schematron V2 Small when cost matters at scale.
| Spec | DeepSeek: DeepSeek V3.1 Terminus (exacto) | Inference.net: Schematron V2 Small |
|---|---|---|
| Provider | DeepSeek | Inference.net |
| Input price (per 1M tokens) | $0.21 | $0.05 |
| Output price (per 1M tokens) | $0.79 | $0.23 |
| Blended price (3:1 input/output) | $0.35 | $0.10 |
| Cheaper than … of all models we track | 61% | 82% |
| Context window | 164K tokens (~246 pages) | 128K tokens (~192 pages) |
| Larger context than … of all models | 35% | 14% |
| Max output per response | n/a | 4K tokens (~3,100 words) |
| Input types | text | text |
| Output types | text | text |
| Free variant | No | No |
| Listed since | 2025-09-22 | 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) | DeepSeek: DeepSeek V3.1 Terminus (exacto) | Inference.net: Schematron V2 Small | Difference |
|---|---|---|---|
| Customer-support chatbot 50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens) | $31.55 | $8.35 | Inference.net: Schematron V2 Small saves $23.20 |
| RAG search over documents 20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens) | $33.10 | $8.30 | Inference.net: Schematron V2 Small saves $24.80 |
| 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) | $34.68 | $8.76 | Inference.net: Schematron V2 Small saves $25.92 |
| Batch summarisation 10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens) | $21.54 | $5.38 | Inference.net: Schematron V2 Small saves $16.16 |
| Long-document analysis 500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens) | $11.29 | $2.73 | Inference.net: Schematron V2 Small saves $8.56 |
| Capability | DeepSeek: DeepSeek V3.1 Terminus (exacto) | Inference.net: Schematron V2 Small | 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 | ✓ 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 | — | — | 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: DeepSeek: DeepSeek V3.1 Terminus (exacto) is the pick when you need tool / function calling, Inference.net: Schematron V2 Small when cost matters at scale. In short: DeepSeek: DeepSeek V3.1 Terminus (exacto) when you need tool / function calling, you need reasoning / thinking mode; Inference.net: Schematron V2 Small when cost matters at scale, you want the newer model.
Inference.net: Schematron V2 Small is cheaper: $0.10 vs $0.35 per 1M tokens at a typical 3:1 input/output mix, 73% less. For 50,000 support conversations a month that is $8.35 against $31.55.
DeepSeek: DeepSeek V3.1 Terminus (exacto) costs $0.21 per 1M input tokens and $0.79 per 1M output tokens; Inference.net: Schematron V2 Small costs $0.05 input and $0.23 output. Output tokens are billed separately, so long answers shift the gap.
DeepSeek: DeepSeek V3.1 Terminus (exacto): 164K tokens, about 246 pages, against 128K (about 192 pages) for Inference.net: Schematron V2 Small.
Tool calling — DeepSeek: DeepSeek V3.1 Terminus (exacto): yes, Inference.net: Schematron V2 Small: not advertised. JSON-schema structured output — DeepSeek: DeepSeek V3.1 Terminus (exacto): yes, Inference.net: Schematron V2 Small: yes.
Neither accepts image input — both are text-in models.
No free variant is listed for either; both are pay-per-token.
DeepSeek: DeepSeek V3.1 Terminus (exacto) was listed on 2025-09-22; Inference.net: Schematron V2 Small on 2026-09-12.
DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's performance in coding and search agents. It is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config) The model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows.
Full DeepSeek: DeepSeek V3.1 Terminus (exacto) specs →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 →