VENICE ATLAS · MODEL DOSSIER · GPT-5.6 LUNA

Model overview

GPT-5.6 Luna

Name: GPT-5.6 Luna
Brand: venice.ai
Price: 0.2000 USD
Availability: InStock

GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series. It is suited for high-volume, latency-sensitive tasks such as chat, classification, and lightweight agentic workflows, providing capable reasoning for its price tier.

openai-gpt-56-luna · venice.ai

Text · Venice catalog$0.20 per 1M tokensPrice on Venice AI

ModalityTextOn Venice AI

Primary price$0.20 per 1M tokensper 1M tokens

Context window1MMax tokens

Benchmarks45Benchmark results

Benchmark results

See the tests behind each score. They use the benchmark’s original scoring scale.

Agents#3529.95 rating Biology#1634.68 rating Chemistry#2034.68 rating Coding#2833.63 rating General intelligence#3844.12 rating Long Context#1521.15 rating Physics#2134.68 rating Safety#816.42 rating Science#183.73 rating Search#1819.96 rating Spatial Reasoning#2222.13 rating Systems#11-0.67 rating Tool Calling#3423.01 rating

Agents17 results

Agents' Last Examagents-last-exam · agents

50.3%

ReportedOct 2026

AutomationBenchautomationbench · agents

14.9%

ReportedOct 2026

Big Finance Benchbig-finance-bench · agents

36.0%

ReportedOct 2026

Capture-the-Flag Challenges (Internal)capture-the-flag-challenges · agents

85.2%

ReportedOct 2026

Connectorsopenai-connectors · agents

99.9%

ReportedOct 2026

ExploitGymexploitgym · agents

12.4%

ReportedOct 2026

GeneBench-Progenebench-pro · agents

10.8%

ReportedOct 2026

Internal Research Debugging Evaluationinternal-research-debugging-evaluation · agents

50.8%

ReportedOct 2026

KernelGen 1Pkernelgen-1p · agents

22.4%

ReportedOct 2026

LifeSciBenchlifescibench · agents

51.2%

ReportedOct 2026

Management Consulting Tasks (Internal)management-consulting-tasks · agents

35.4%

ReportedOct 2026

NanoGPTnanogpt · agents

1.66%

ReportedOct 2026

PostTrainBench Liteposttrainbench-lite · agents

29.6%

ReportedOct 2026

RSI Indexrsi-index · agents

41.9%

ReportedOct 2026

Search and Function-Callingopenai-search-function-calling · agents

89.7%

ReportedOct 2026

SEC-bench Prosec-bench-pro · agents

48.9%

ReportedOct 2026

Toolathlontoolathlon · agents

53.4%

ReportedOct 2026

Coding5 results

BenchCADbenchcad · code

63.1%

ReportedOct 2026

BenchCAD (with Python tool)benchcad-with-python-tool · code

73.9%

ReportedOct 2026

DeepSWEdeepswe · code

67.2%

ReportedOct 2026

DeepSWE 1.1deepswe-1.1 · code

67.0%

ReportedOct 2026

ExploitBenchexploitbench · code

33.2%

ReportedOct 2026

General intelligence2 results

Artificial Analysisartificial-analysis · general

51.0%

ReportedOct 2026

GDP.pdfgdp-pdf · general

22.7%

ReportedOct 2026

Healthcare4 results

HealthBenchhealthbench · healthcare

55.8%

ReportedOct 2026

HealthBench Consensushealthbench-consensus · healthcare

95.1%

ReportedOct 2026

HealthBench Hardhealthbench-hard · healthcare

32.0%

ReportedOct 2026

HealthBench Professionalhealthbench-professional · healthcare

55.7%

ReportedOct 2026

Long Context2 results

MRCR v2 (8-needle, 512K-1M)mrcr-v2-8-needle-512k-1m · long_context

41.3%

ReportedOct 2026

MRCR v2 (8-needle)mrcr-v2-(8-needle) · long_context

41.3%

ReportedOct 2026

Learn what each benchmark measures ↗

Pricing detail

Input price per 1M tokens: $0.20
Output price per 1M tokens: $1.20
Cache read price per 1M tokens: $0.0200
Cache write price per 1M tokens: $0.25
Blended price per 1M tokens (50/50): $0.70

Prices are published by Venice AI. Venice model page ↗

Capabilities

ReasoningFunction callingVisionWeb search

Endpoint metadata

Privacy: anonymized
Quantization: not-available
Maximum completion tokens: 128K
Uncensored: No
Beta: No
Mapping confidence: 100.0%

View model source ↗

All capabilities

Max Images: 10
Quantization: not-available
Supports E2 E E: No
Supports Vision: Yes
Supports X Search: No
Optimized For Code: No
Supports Log Probs: No
Supports Reasoning: Yes
Supports Web Search: Yes
Supports Audio Input: No
Supports Video Input: No
Default Reasoning Effort: high
Reasoning Effort Options: none, low, medium, high, xhigh, max
Supports Multiple Images: Yes
Supports Response Schema: Yes
Supports Tee Attestation: No
Supports Function Calling: Yes
Supports Reasoning Effort: Yes

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