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AI coding models

GPT-6.1 Sol vs. Claude Sonnet 5.5 in Copilot: Which Should You Choose?

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Start with GPT-6.1 Sol for a complex coding problem where careful reasoning is central; start with Claude Sonnet 5.5 for general-purpose coding or an agent workflow. That is GitHub’s stated positioning, not proof that either model is more accurate or faster. Both have the same listed per-token rates in GitHub Copilot’s default and long-context tiers, so the better choice depends on the task, availability in your Copilot surface, and how the model performs in your workflow.

Which Copilot model should you use?

Your task Starting point Why
A complex coding problem where careful reasoning is important GPT-6.1 Sol GitHub categorizes it for complex coding tasks and describes it as offering advanced, efficient reasoning.
General-purpose coding or an agent task Claude Sonnet 5.5 GitHub categorizes it for general-purpose coding and agent tasks, describing efficient completion with fewer steps, tokens, and tool calls.
You want to know which is better overall Try both on a representative task GitHub’s descriptions are product positioning, not a controlled head-to-head benchmark.

These are useful starting points, not rules. A model’s stated task fit does not guarantee the best result on your particular codebase or request. GitHub’s AI model comparison does not establish a universal winner for these two models.

How to compare them on your own work

If both appear in your model picker, run the same representative task with each and judge the result against the same criteria. Keep the prompt, repository context, and success conditions as consistent as possible.

  • Correctness: Does the change work, and does it handle the relevant edge cases?
  • Completeness: Did it address the full request without unrelated changes?
  • Tool use: How many steps or tool calls did the task take, and were they useful?
  • Time: How long did the workflow take in your actual client and environment?
  • AI-credit use: What did the interaction consume? Matching token rates do not mean two tasks will use the same number of tokens.

This kind of comparison is more informative than choosing by model description alone. It is a practical evaluation method, not a claim that one model has been tested here.

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Do they cost the same in GitHub Copilot?

GitHub’s Copilot pricing table lists matching input and output rates for both models in the default and long-context tiers. The figures below are GitHub Copilot billing rates per million tokens, not the models’ provider API prices or the total price of completing a task.

Copilot pricing tier GPT-6.1 Sol Claude Sonnet 5.5
Default: input $2.00 per million tokens $2.00 per million tokens
Default: cached input $0.10 per million tokens $0.20 per million tokens
Default: cache write $2.50 per million tokens $2.50 per million tokens
Default: output $10.00 per million tokens $10.00 per million tokens
Long context: input $4.00 per million tokens $4.00 per million tokens
Long context: cached input $0.20 per million tokens $0.20 per million tokens
Long context: cache write $5.00 per million tokens $5.00 per million tokens
Long context: output $15.00 per million tokens $15.00 per million tokens

The exception in the default tier is cached input: GitHub lists a lower rate for GPT-6.1 Sol than for Claude Sonnet 5.5. Otherwise, the listed rates match. Rates and model availability can change; check GitHub’s Copilot models and pricing for the current table. Even where rates match, total AI-credit use can differ because token counts and interaction patterns vary.

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Check that you can select the model

Being listed in GitHub’s documentation does not mean a model is selectable in every Copilot plan, client, or workflow. GitHub says model availability varies by plan and product surface, and organization administrators can restrict access. Its supported-model documentation lists both GPT-6.1 Sol and Claude Sonnet 5.5; check the model picker in the surface you actually use.

For Copilot cloud agent, GitHub lists both models as options, but model selection is supported only in specified task entry points. If a picker is not available, Copilot uses Auto, which chooses based on availability and to help reduce rate limiting. Do not assume your preferred model ran when you could not select it; confirm the model used where the product exposes that information. See GitHub’s cloud-agent model-selection instructions for the applicable entry points and current behavior.

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What the published comparison does—and does not—show

GitHub marks both models as generally available in its Copilot billing table. Its model comparison describes their intended task fit, but the reviewed product pages do not provide a controlled direct benchmark showing that one is more accurate, faster, or better overall. Treat performance descriptions as guidance from GitHub, not independent test results. For a decision that matters to your work, use the model that is available and compare results on your own representative task.

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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.

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