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OpenAI: GPT-5.6 Sol Coding Benchmark

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 series. It is suited for complex reasoning, coding, and agentic workflows, and is particularly strong at command-line and multi-step coding tasks...

Context1,050,000tokens
Max Output128,000tokens
Inputmodalities
Price$4.00/1M input
Ranks from Kilo Code LeaderboardPricing via OpenRouter

Try OpenAI: GPT-5.6 Sol in Kilo Code

Experience this model with the most popular open source coding agent. Free to start, pay only for AI usage. Use in popular IDEs like VS Code, JetBrains, command line, or cloud agents.

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Coding Performance

Coding benchmarks and performance metrics for development tasks

Kilo Bench

% Completion on Terminal Bench 2.0
76.2%
Cost per attempt (USD)
$87.41
Benchmark
Terminal Bench 2.0

Official Kilo eval results. Cost is averaged per complete benchmark attempt.

Security

Enkrypt AI red-team scores for OpenAI: GPT-5.6 Sol. Each value is the share of successful attacks on a 0–100 scale — lower is safer.

Overall risk
21.6

Lower is safer

Safety

31.8/ 100

Composite safety risk from Enkrypt red-team evaluations. Lower is safer.

NIST

22.0/ 100

Average attack success across NIST-mapped tests: bias, harm, toxicity, CBRN, and insecure code.

OWASP

25.0/ 100

Weighted average of the same tests using OWASP Top 10 for LLMs 2025 risk rankings.

Attack categories

Percentage of successful attacks in each Enkrypt red-team category.

Jailbreak9.2/ 100

Share of jailbreak tests that bypassed the model's safety constraints.

Bias72.1/ 100

Share of tests that elicited biased responses.

Harmful content0.6/ 100

Share of tests that produced dangerous, violent, or hateful content.

Toxicity8.9/ 100

Share of tests that produced toxic or abusive content.

CBRN19.2/ 100

Share of tests that elicited chemical, biological, radiological, or nuclear assistance.

Insecure code7.1/ 100

Share of tests that produced vulnerable or malicious code.

Score
Value
Overall risk
21.6 / 100
Safety
31.8 / 100
NIST
22.0 / 100
OWASP
25.0 / 100
Jailbreak
9.2 / 100
Bias
72.1 / 100
Harmful content
0.6 / 100
Toxicity
8.9 / 100
CBRN
19.2 / 100
Insecure code
7.1 / 100

Security scores from the Enkrypt AI Safety Leaderboard · Last checked Sep 17, 2026

Real-World Usage

Real-world usage statistics from the Kilo Code community

Weekly Token Usage

Mode Rankings (Last Week)

Where this model ranks for each built-in mode

Code

Write, modify, and refactor code

No data

Ask

Get answers and explanations

#97

Debug

Diagnose and fix software issues

#11

Orchestrator

Coordinate tasks across multiple modes

No data

Real-world metrics from the Kilo Code Leaderboard

OpenClaw Benchmarks

PinchBench measures how OpenAI: GPT-5.6 Sol performs on real OpenClaw agent tasks: multi-step execution, tool use, recovery, latency, and cost.

PinchBench run

Average score

84.2%

#13 of 50 official models

Average time

265m 16s

5 runs · per OpenClaw task

Average cost

$69.232

Per benchmark run

Category breakdown

Best verified PinchBench v2 run by OpenClaw task family.

Memory100.0% · 2/2 cleared
Integrations96.4% · 0/3 cleared
Productivity96.0% · 4/8 cleared
Analysis95.4% · 5/12 cleared

Top task results

Highest-scoring benchmark tasks from the same submission.

Analysis
Access Control Log Anomaly Detection
100.0%
Log Analysis
Apache Error Log - Identify Problematic Client IPs
100.0%
Productivity
Calendar Event Creation
100.0%
Skills
Create Project Structure
100.0%
Research
Executive Lookup
100.0%
Skills
File Structure Creation
100.0%

Autonomous task execution

OpenAI: GPT-5.6 Sol shows strong average success across OpenClaw-style benchmark runs, useful for recurring research, browser, and file-based automations.

Tool use and recovery

PinchBench tasks stress multi-step planning, tool calls, and judge-verified completion rather than single prompt coding snippets.

Agent workflow fit

Its deliberate average runtime and premium run cost help set expectations for long-running agents and production workflows.

Agentic benchmarks from the PinchBench Leaderboard

Pricing

Cost per 1 million tokens

Input Tokens
$4.00
per 1M tokens
Output Tokens
$20.00
per 1M tokens

Example Cost

Analyzing a 10,000 line codebase (≈40k input tokens, 10k output tokens) costs approximately $0.3600

Coding Capabilities

Features and parameters relevant to coding tasks

Coding Features

Function Calling
Can call external functions/APIs
Tool Choice
Control over function selection
Structured Outputs
JSON schema validation
Reasoning Tokens
Extended thinking for complex problems

Pricing details from OpenRouter

Technical Details

Architecture and implementation specifications

Specifications
Model ID
openai/gpt-5.6-sol
Created
July 9, 2026
Tokenizer
GPT
Input Modalities
FileImageText
Context Window
1,050,000 tokens
Max Completion Tokens
128,000 tokens
Input Price
$4.00 per 1M tokens
Output Price
$20.00 per 1M tokens
Cache Read Price
$0.40 per 1M tokens
Cache Write Price
$5.00 per 1M tokens
Content Moderation
Enabled

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