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Favicon for respan

Respan: Span-01

respan/span-01

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Span-01 is a behavior scoring model from Respan. It reads a conversation span and returns, for each plain-language behavior you define, the probability that the behavior is present. It is suited for evaluation, guardrails, and monitoring of LLM and agent outputs at scale.

It is the higher-accuracy tier of the family. Span-01 Lite is the free, lighter tier.

Modalities
In / out price
$0.02 / $0per 1M
Released
Sep 26, 2026
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PlaygroundProvidersPricingPerformanceUptimeAppsActivityDocumentationFAQExplore

Playground

Providers

This model is hosted by one provider. OpenRouter forwards every request to it directly — no routing decisions to make.

Pricing

The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.

Performance

Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

Activity

Token volume and request traffic to this model over time.

API

Drop-in code to call this model. It runs on the OpenRouter Decisions API rather than the OpenAI-compatible chat endpoint, so the request and response shapes below are specific to structured decisions — chat completions SDKs will not work with it.

Not currently available through EU in-region routing. In-region routing docs

Documentation

Guides, cookbooks, blog posts, and tools for building with Respan: Span-01 on OpenRouter.

  • Decisions LabLab

Explore more models

Decision Model RankingsRanking
Latency
$0.02Free0.45s
100.00%
ProviderInput / Output /M
LatencyUptime
  • Favicon for Respan
    Input $0.02 / Output Free
    Latency 0.45sUptime 100.00%
Latency
0.45s

P50, best provider

Frequently asked questions

Span-01 is a behavior scoring model from Respan. It reads a conversation span and returns, for each plain-language behavior you define, the probability that the behavior is present. It is suited for evaluation, guardrails, and monitoring of LLM and agent outputs at scale. It is the higher-accuracy tier of the family. Span-01 Lite is the free, lighter tier.

The Span-01 documentation on OpenRouter includes the interactive Decisions Lab. The quickstart covers API keys and your first request, and the API reference lists every parameter.

Span-01 costs $0.02/M input tokens and $0.00/M output tokens.

Span-01 accepts text as input and returns structured decisions.

Span-02 and Span-01 Lite are other text models from Respan.

Span-01 was released on September 26, 2026.

More models from Respan

Span-02

Span-02 is an open-weight decision model from Respan. It reads a state of text and images and answers the questions you define about it: picking one of several options, a yes/no/unknown call, or a score on a scale. It is suited for evaluation, guardrails, routing, and monitoring of LLM and agent outputs at scale.

It is the successor to Span-01.

Decisions$0.01 / $0
Span-01 Lite

Span-01 Lite is the free, lighter tier of Span-01, a behavior scoring model from Respan. It returns, for each plain-language behavior you define, the probability that the behavior is present in a conversation span, and is suited for high-volume evaluation and monitoring where cost matters more than peak accuracy.

DecisionsFree
Span-01 Lite (free)

Span-01 Lite is the free, lighter tier of Span-01, a behavior scoring model from Respan. It returns, for each plain-language behavior you define, the probability that the behavior is present in a conversation span, and is suited for high-volume evaluation and monitoring where cost matters more than peak accuracy.

DecisionsFree