Best AI coding models: choose for the mission

As of October 31, 2026, candidates to evaluate include GPT-6 Astra and GPT-6.1, Claude 5.5, Gemini 4 Argon and Gemini 3.8 Flash. Gemini 4 Carbon has been announced with restricted initial access. The right choice depends on the mission, available tools and cost of an accepted outcome.

This guide provides a shortlist and evaluation method, rather than claiming a field benchmark conducted by Stellary. Named comparisons and earlier generations retain their historical context.

Select candidates by task

Model names do not guarantee availability in a particular tool or workspace. API access, application access and a Stellary catalog entry are distinct states.

Check sources and access

Official OpenAI documentation describes its current lineup. Anthropic announcements identify Claude releases. The Gemini catalog describes Google's available models. For self-hosting, inspect weights and license, as in the Mistral Large 4 release.

Check prices when selecting a model. Record its exact identity, service, account and trial conditions. Introductory prices, caching and subscriptions can change the comparison. A local token estimate is not authoritative billing.

Keep the environment comparable

A coding agent combines a model with repository access, retrieval, terminal, browser and permissions. Cursor, Claude Code and Codex are therefore not simply additional model names in the same comparison column.

When replacing a model, preserve inputs and tools where possible. Describe changes in execution engine. A better outcome might come from better context retrieval or a verification flow available in one environment and absent in another.

AGENTS.md and CLAUDE.md instructions make the frame explicit. They do not replace effective permissions or establish identical behavior across tools.

Choose tasks that expose differences

Build a small representative set: a bounded fix, a contract change, an ambiguous investigation and an independent review. Define criteria before looking at outputs.

Include plausible results that fail in the product: wrong exported filters, authorization failures or data lost after signing back in. Evaluate the journey and its invariants instead of favoring the most convincing explanation.

Independent review matters for consequential changes. The authoring model already knows its own justification; another inspection may find what it missed. See task-specific backend and code review criteria.

Measure cost per accepted outcome

Include available usage figures, retries and human effort. A lower per-token rate can require more attempts. A slower model can be worthwhile if it reduces corrections; the reverse can also happen.

Elapsed time begins at mission launch and ends at acceptance. Separate tool and approval waiting where observable. Retain incomplete outcomes instead of removing them from the comparison.

Compare correctness, scope discipline, evidence quality, elapsed time and observable cost. One score cannot represent every project. Revisit the selection when missions or models change.

Assign models to roles

A workflow can give exploration, creation and verification different configurations. Start with few roles, then add a specialization when it improves an observed result.

A local model may fit a bounded step while a difficult investigation justifies another configuration. Define data flow and tool capabilities. This division does not establish universal automatic routing.

The supervisor workflow guide describes handoff contracts. The pilot needs usable deliverables rather than disconnected conversations.

Announced models and forthcoming access

The October 28 Carbon announcement describes a progressive rollout. Our Carbon article develops evaluation questions. Mistral discusses future specialized models in its Large 4 announcement without naming or dating every variant there.

Keep a stable URL for the current selection. New access justifies a sourced update and suitable trials; it does not justify replacing every model used by the team.

Connect model selection to delivery

Choose a mission, an accessible model and an authorized environment. Preserve its outcome and review. If several contributions are required, use coordination to track dependencies.

The complete application guide connects framing, implementation, verification and deployment. A model serves this workflow; its value appears in work the team can actually accept and use.

A bounded comparison you can record now

Download the evaluation worksheet and CSV log. Both are blank: no model scores or results are prefilled.

Choose two accessible configurations and one small correction on a project copy. Define the defect, expected result and an edge case. Give both configurations the same starting state and task, then preserve every run, including failures. Where practical, have someone review the result without knowing the model name.

One run describes a case, not general superiority. Repeat the tasks that affect your decision and publish the protocol before making a numerical claim. In Stellary, attach the worksheet and evidence to the task carrying the comparison.