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databricks-gpt-5-nano

databricks · Chat model

databricks-gpt-5-nano is listed here as a chat model from databricks. This page shows simple API pricing, token limits, and capability flags so you can compare it with similar options.

Provider and model identifiers are kept in their original form for accuracy.

databricks-databricks-databricks-gpt-5-nano

Input
$0.0500 / 1M tokens
Output
$0.4000 / 1M tokens
Cached input
N/A
Context window
128.0K

Catalog generated: Jul 13, 2026

Compare this model Open a side-by-side table using this route and a close match from the current catalog. Estimate cost Use token counts, request volume, and cache assumptions to check a realistic monthly bill.

Quick read

Best for

Use this page when you need a fast view of cost, context size, and supported features before testing the model in your own workload.

Things to verify

Always check the provider page for discounts, cache pricing, region rules, and any model limits that may not appear in public metadata.

Pricing

Item Price
Input
$0.0500 / 1M tokens
Output
$0.4000 / 1M tokens
Embedding
$0.0500 / 1M tokens

Limits

Context window
128.0K
Max input tokens
272.0K
Max output tokens
128.0K
Max tokens
128.0K
Explore benchmark family Review source-backed benchmark rows for the attached base model family. Check data sources See how pricing data is generated and what still needs provider verification. API vs subscription Learn when API pricing matters compared with ChatGPT, Claude Code, Cursor, or Copilot subscriptions. Cache and batch pricing Understand cached input, cache write, batch, priority, and flex pricing rows shown on this page.

Capabilities

Capability Supported
Vision -
Function calling -
Parallel function calling -
Tool choice -
Prompt caching -
Reasoning -
Response schema -
System messages -
Audio input -
Audio output -
Web search -
PDF input -
Video input -

Benchmarks

Most benchmark rows are attached to the base model family rather than this provider route. Open benchmark explorer

Benchmark Score Metric Scope Checked Source
SWE-bench Verified 74.9% accuracy (%) Base model: gpt-5 (GPT-5 (high)) 2026-05-31 Link
Aider Polyglot 88.0% accuracy (%) Base model: gpt-5 (GPT-5 (high)) 2026-05-31 Link
GPQA Diamond 85.7% accuracy (%) Base model: gpt-5 (GPT-5 (high)) 2026-05-31 Link
AIME 2025 94.6% accuracy (%) Base model: gpt-5 (GPT-5 (high)) 2026-05-31 Link
Tau2-bench Telecom 96.7% accuracy (%) Base model: gpt-5 (GPT-5 (high)) 2026-05-31 Link
Aider Polyglot 88.0% percent correct Base model: GPT-5 (openai/gpt-5) 2026-05-31 Link
Aider Polyglot 86.7% percent correct Base model: GPT-5 (openai/gpt-5) 2026-05-31 Link
Aider Polyglot 81.3% percent correct Base model: GPT-5 (openai/gpt-5) 2026-05-31 Link
AIME 2025 85.2% accuracy (%) Base model: gpt-5-nano (GPT-5 nano (high)) 2026-05-31 Link

Sources

Source links
Pricing dataLiteLLM model cost map
Synced at2026-05-28
Catalog generated2026-07-13T14:21:55.110Z