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GPT-5 Codex vs GPT-6 Sol

GPT-6 Sol leads the LLM Stats Score 49.4 to 26.6.

OpenAI · OpenAI · Updated for 2026

Which is better?

GPT-6 Sol leads the overall LLM Stats Score 49.4 to 26.6, ranking #21 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose GPT-5 Codex

  • you are already invested in the OpenAI ecosystem

Choose GPT-6 Sol

  • overall performance matters — it scores 49.4 and ranks #21 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Sep 2026

At a glance

The differences that matter most.

GPT-5 Codex
OpenAI
GPT-6 Sol
OpenAI
Core performance indexes
LLM Stats Score
26.6
#155
49.4
#21
Reasoning
26.5
#153
45.6
#29
Coding
19.3
#112
35.8
#25
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$2.00 / M
Output price
— / M
$10.00 / M
Context window
—
1,050,000

Individual benchmarks

1 reported for GPT-5 Codex · 5 for GPT-6 Sol

No common benchmarks found

GPT-5 Codex and GPT-6 Soldon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

Only GPT-6 Sol specifies input context (1,050,000 tokens). Only GPT-6 Sol specifies output context (128,000 tokens).

OpenAI
GPT-5 Codex
Input- tokens
Output- tokens
OpenAI
GPT-6 Sol
Input1,050,000 tokens
Output128,000 tokens
Tue Sep 22 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

GPT-6 Sol supports multimodal inputs, whereas GPT-5 Codex does not.

GPT-6 Sol can handle both text and other forms of data like images, making it suitable for multimodal applications.

GPT-5 Codex

Text
Images
Audio
Video

GPT-6 Sol

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

GPT-5 Codex

Proprietary

Closed source

GPT-6 Sol

Proprietary

Closed source

Release Timeline

When each model was launched

GPT-5 Codex was released on 2025-09-15, while GPT-6 Sol was released on 2026-09-22.

GPT-6 Sol is 12 months newer than GPT-5 Codex.

GPT-5 Codex

Sep 15, 2025

1.0 years ago

GPT-6 Sol

Sep 22, 2026

0 days ago

1.0yr newer

Knowledge Cutoff

When training data ends

GPT-5 Codex has a knowledge cutoff of 2024-09-30, while GPT-6 Sol has a cutoff of 2026-04-20.

GPT-6 Sol has more recent training data (up to 2026-04-20), making it potentially better informed about events through that date compared to GPT-5 Codex (2024-09-30).

GPT-5 Codex

Sep 2024

GPT-6 Sol

Apr 2026

1.6 yr newer

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against GPT-5 Codex and GPT-6 Sol side-by-side, then vote on the output you prefer.

GPT-5 Codex
✓ Preferred
GPT-6 Sol
Open in Playground

FAQ

Common questions about GPT-5 Codex vs GPT-6 Sol.

Which is better, GPT-5 Codex or GPT-6 Sol?

GPT-6 Sol leads the LLM Stats Score 49.4 to 26.6. GPT-5 Codex is made by OpenAI and GPT-6 Sol is made by OpenAI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does GPT-5 Codex compare to GPT-6 Sol in benchmarks?

GPT-5 Codex scores SWE-Bench Verified: 74.5%. GPT-6 Sol scores DeepSWE 1.1: 68.8%, OSWorld 2.0: 64.4%, Agents' Last Exam: 56.4%, FrontierCode 1.1: 49.3%, AutomationBench v1.0.6: 33.2%.

What are the context window sizes for GPT-5 Codex and GPT-6 Sol?

GPT-5 Codex supports an unknown number of tokens and GPT-6 Sol supports 1.1M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GPT-5 Codex and GPT-6 Sol?

Key differences include LLM Stats Score (26.6 vs 49.4), multimodal support (no vs yes). See the full comparison above for benchmark-by-benchmark results.

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