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OpenAI: GPT-4.1 Nano (batch)

openai/gpt-4.1-nano:batch

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For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million token context window, and scores 80.1% on MMLU, 50.3% on GPQA, and 9.8% on Aider polyglot coding – even higher than GPT‑4o mini. It’s ideal for tasks like classification or autocompletion.

Modalities
In / Out Price
$0.05 / $0.20per 1M
Context
1.0M
Released
Apr 14, 2025
Knowledge Cutoff
Jun 2024
Compare
ProvidersPricingPerformanceUptimeBenchmarksAppsActivityFAQExplore

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), Floor (cheapest), or Exacto (highest tool-calling accuracy).

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.

Benchmarks

Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.

Benchmark score summary for OpenAI: GPT-4.1 Nano (batch) (Artificial Analysis and Design Arena)
SourceBenchmarkScore
Artificial AnalysisGPT-4.1 nano Coding Index11.1
Artificial AnalysisGPT-4.1 nano GPQA Diamond51.2%
Artificial AnalysisGPT-4.1 nano HLE3.8%
Artificial AnalysisGPT-4.1 nano IFBench32.0%
Artificial AnalysisGPT-4.1 nano τ²-Bench Telecom17.3%
Artificial AnalysisGPT-4.1 nano AA-LCR20.3%
Artificial AnalysisGPT-4.1 nano GDPval-AA0.0%
Artificial AnalysisGPT-4.1 nano CritPt0.0%
Artificial AnalysisGPT-4.1 nano Terminal-Bench Hard3.8%
Artificial AnalysisGPT-4.1 nano AA-Omniscience Accuracy13.7%
Artificial AnalysisGPT-4.1 nano AA-Omniscience Non-Hallucination Rate17.4%
Design ArenaGPT-4.1 nano Models Arena 3D Elo943
Design ArenaGPT-4.1 nano Models Arena Code Categories Elo972
Design ArenaGPT-4.1 nano Models Arena Data Visualization Elo899
Design ArenaGPT-4.1 nano Models Arena Game Development Elo983
Design ArenaGPT-4.1 nano Models Arena UI Component Elo926
Design ArenaGPT-4.1 nano Models Arena Website Elo980

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. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.

Explore more models

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$0.05$0.20$0.0125----
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AutoExacto benchmarks
GPQA DiamondTAU-BenchAzure50.8%14.0%OpenAI50.5%13.3%Azure51.2%12.0%auto-routing50.0%10.0%

Not enough uptime data to display yet.

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and customization options.

Not enough data for apps using this model yet.

Frequently asked questions

For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million token context window, and scores 80.1% on MMLU, 50.3% on GPQA, and 9.8% on Aider polyglot coding – even higher than GPT‑4o mini. It’s ideal for tasks like classification or autocompletion.

GPT-4.1 Nano (batch) costs $0.05/M input tokens and $0.20/M output tokens, with separate rates for Cache Read at $0.0125/M tokens and Web Search at $10.00/1K calls.

GPT-4.1 Nano (batch) has a 1,047,576 token context window. It supports up to 32,768 completion tokens.

Yes. GPT-4.1 Nano (batch) accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.

GPT-4.1 Nano (batch) accepts images, text, and files such as PDFs as input and returns text.

GPT-6.1 Sol Pro, GPT-6.1 Sol, GPT-6 Luna Pro and 61 more are other text models from OpenAI.

GPT-4.1 Nano (batch) was released on April 14, 2025. Its knowledge cutoff is June 30, 2024.

More models from OpenAI

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GPT-6.1 Sol Pro is the same underlying model as GPT-6.1 Sol, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6.1 Sol and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6.1 Sol instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

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GPT-6.1 Sol Pro

GPT-6.1 Sol Pro is the same underlying model as GPT-6.1 Sol, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6.1 Sol and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6.1 Sol instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

Text1.1M context$1 / $5
GPT-6.1 Sol

GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series. It is suited for agentic coding, computer use, document-heavy professional work, and multi-step business workflow automation, and approaches Astra-level results on these tasks at a much lower cost. Compared with GPT-6 Sol, it makes fewer factual errors and is more reliable at respecting explicit restrictions and user intent during agentic tasks.

Text1.1M context$2 / $10
GPT-6.1 Sol

GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series. It is suited for agentic coding, computer use, document-heavy professional work, and multi-step business workflow automation, and approaches Astra-level results on these tasks at a much lower cost. Compared with GPT-6 Sol, it makes fewer factual errors and is more reliable at respecting explicit restrictions and user intent during agentic tasks.

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GPT-6 Luna Pro

GPT-6 Luna Pro is the same underlying model as GPT-6 Luna, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6 Luna and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6 Luna instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

Text1.1M context$0.10 / $0.50
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Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6 Luna and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6 Luna instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

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Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

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Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

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