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There is no single best LLM for coding across every task in 2026. In Anthropic’s May 2026 comparison, Claude Opus 4.8 leads GPT-5.5 and Gemini 3.1 Pro on SWE-bench Pro, while GPT-5.5 leads the same comparison on Terminal-Bench 2.1. Results change with the benchmark, its version and the agent harness; the available figures are vendor-published, not a neutral test of all three models in one environment.
For a real choice, match the model to the work you need done, test it in your intended coding setup, and assess the governance terms of the exact product or API deployment. This comparison reflects official materials available through October 7, 2026; benchmark results are snapshots, not guarantees of performance in your repository.
What the published coding benchmarks show
Anthropic’s Claude Opus 4.8 System Card, dated May 2026, contains a three-model comparison. The table below preserves its benchmark names and versions. These are figures published by Anthropic, not results independently reproduced here. “Not stated” means the card’s cited comparison does not report a score for that model and benchmark in the available evidence.
| Benchmark and version | Claude Opus 4.8 | GPT-5.5 | Gemini 3.1 Pro |
|---|---|---|---|
| SWE-bench Verified | 88.6% (Anthropic, May 2026) | Not stated (Anthropic, May 2026) | Not stated (Anthropic, May 2026) |
| SWE-bench Pro | 69.2% (Anthropic, May 2026) | 58.6% (Anthropic, May 2026) | 54.2% (Anthropic, May 2026) |
| Terminal-Bench 2.1 | 74.6% (Anthropic, May 2026) | 78.2% (Anthropic, May 2026); 83.4% when reported with the Codex CLI harness | 70.3% (Anthropic, May 2026) |
| BrowseComp | 84.3% single-agent; 88.5% multi-agent (Anthropic, May 2026) | 84.4% (Anthropic, May 2026) | 85.9% (Anthropic, May 2026) |
| OSWorld-Verified | 83.4% (Anthropic, May 2026) | 78.7% (Anthropic, May 2026) | 76.2% (Anthropic, May 2026) |
The 83.4% GPT-5.5 Terminal-Bench figure uses the Codex CLI harness, so it is a different setup from the 78.2% result. Do not treat the two as interchangeable or combine them into one ranking. More broadly, a benchmark score is meaningful only with its version and setup: harness, prompting, inference settings, number of attempts and reporting date can all affect what is being measured.
#1 Best Overall
Why Gemini’s separate model-card scores differ
Google DeepMind’s Gemini 3.1 Pro Model Card reports a separate set of results as of February 2026. It lists 80.6% on SWE-bench Verified for a single attempt, 54.2% on SWE-bench Pro (Public) for a single attempt, and 68.5% on Terminal-Bench 2.0 using the Terminus-2 harness. Those figures should not be spliced into Anthropic’s May three-way table: the cards use different dates, versions, harnesses and evaluation setups.
The Gemini card also reports long-context results: 84.9% on MRCR v2 at 128k (average) and 26.3% at 1M (pointwise). The card notes that some compared models do not support the 1M evaluation. These measurements can inform a long-context workload assessment, but they do not establish that a model will understand or modify a large codebase better in your particular tools and repository.
Which model may fit your coding work?
Use the scores as clues about task-specific performance, not as a universal league table. Choose a short list based on the work your team actually does, then evaluate each candidate in the intended IDE or coding agent, with your normal repository permissions and review process.
Issue resolution and repository maintenance
For work resembling SWE-bench Pro, Anthropic’s May 2026 card gives Opus 4.8 the highest score in its three-model comparison: 69.2%, versus 58.6% for GPT-5.5 and 54.2% for Gemini 3.1 Pro. This is a reason to include Opus 4.8 in an evaluation for issue-driven code changes, not proof it will be best on your team’s issue mix or codebase. The card also reports 88.6% for Opus 4.8 on SWE-bench Verified, but the cited three-way evidence does not provide matching scores for the other two models on that benchmark.
Terminal work and tool-driven agents
In Anthropic’s Terminal-Bench 2.1 comparison, GPT-5.5 scores 78.2%, Opus 4.8 scores 74.6%, and Gemini 3.1 Pro scores 70.3%. A separate 83.4% GPT-5.5 result is reported with the Codex CLI harness. Since that harness differs, use the result closest to your intended agent setup and validate it there; benchmark harnesses are part of the system being evaluated, not a neutral wrapper around the model.
Long-context repository analysis
Gemini 3.1 Pro’s February 2026 MRCR v2 figures provide evidence about a particular long-context evaluation, including a 1M pointwise result. They do not demonstrate a general advantage for code generation, debugging or repository maintenance. If large context is central to your workflow, test realistic tasks that require locating relevant files, preserving architectural constraints and grounding changes in the repository rather than relying on context-window capacity alone.
Rank #3
Code generation, review and multi-step changes
The cited figures do not settle every common coding task. They do not provide a common three-way evaluation for ordinary code generation or code review, nor do they establish a best model for every multi-step change. Build a small, representative test set from your own work: for example, a bug with a reproducible test, a refactor with explicit compatibility constraints, and a review task with known defects. Score correctness, test behavior, instruction following and the amount of human repair required.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsHow to run a useful model evaluation
- Define the work. Separate issue fixes, terminal operation, code generation, review and long-context analysis. A single blended score can hide a model’s strengths and weaknesses.
- Keep the setup consistent. Use the same repository snapshot, task instructions, tools, permissions, time limits and scoring rules where possible. Record the exact model version, product or API path, harness and date.
- Measure outcomes that matter. Track whether the change is correct, whether tests pass, how often the agent needs intervention, what it edits outside scope, and how much review or rework follows.
- Include operating costs. Measure latency and cost on the service configuration you would actually deploy. The cited materials do not establish an aligned three-way cost or latency comparison, so published benchmark scores cannot answer those procurement questions.
- Test within your policy boundaries. Evaluate repository access, connectors, command execution, logging and human review under the controls you intend to use in production.
Enterprise governance: evaluate the deployment, not just the model
Governance depends on the exact provider product, plan, API route and contract. A model card or safety evaluation is not a substitute for checking administration, data handling, residency, compliance commitments, billing and tool access for the deployment your organization will buy.
Governance checks for procurement
- Identity and administration: confirm SSO, user provisioning, role management and access to audit events.
- Data handling: establish retention, use of submitted data for training, logging and deletion terms for the selected consumer, team, enterprise or API tier.
- Encryption and residency: confirm key-management options and where inference and stored data are processed.
- Compliance scope: verify which certifications and contractual commitments apply to your organization and workload, including any eligibility conditions.
- Cost controls: determine whether usage is included or billed separately, whether tokens are charged, and how administrators set limits.
- Tools and repositories: review connectors, repository permissions, agent execution boundaries, logging and required human approvals.
What the reviewed provider materials establish
Anthropic’s Enterprise help page, dated September 1, 2026, lists audit logs, SCIM, custom retention controls, a Compliance API, an Analytics API, customer-managed encryption keys, US-only inference, spend limits and workplace connectors including GitHub. It also describes HIPAA-readiness for eligible organizations. In the usage-based Enterprise plan described on that page, usage is billed separately at standard API rates. Confirm that the specific controls and terms apply to the offer and contract under consideration; these details are Anthropic-specific and should not be assumed to apply to other providers.
Google DeepMind’s Gemini 3.1 Pro Model Card identifies Google Cloud/Vertex AI and Gemini Enterprise among the model’s distribution channels and points to applicable service terms. The reviewed material does not establish a complete comparison of Google’s enterprise administration, data handling, residency or contractual controls against the other providers.
OpenAI’s GPT-5.5 System Card describes predeployment safety evaluations, Preparedness Framework evaluations, red-teaming and deployment safeguards. It is a safety card, not a full account of enterprise administration, retention, residency or contract terms for every GPT-5.5 access path. Check the exact product and applicable terms before treating a control as available for your deployment.
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- Claude Opus 4.8: Anthropic’s announcement is dated May 28, 2026, and says the model is available through the Claude API as
claude-opus-4-8. Anthropic’s system-card index lists its May 2026 system card. - Gemini 3.1 Pro: Google DeepMind’s model card presents evaluation results as of February 2026 and identifies distribution through the Gemini App, Google Cloud/Vertex AI, Google AI Studio, Gemini API, Google Antigravity, Gemini Enterprise and NotebookLM.
- GPT-5.5: OpenAI’s System Card is dated April 23, 2026; its page notes an April 24 update and an August 19 correction to one reported safety-evaluation figure. The reviewed document is a safety card, not a harmonized coding benchmark report.
How to interpret product claims
Anthropic’s May 28, 2026 announcement republishes a statement from Tom Pritchard, a staff engineer: “Claude Opus 4.8 has noticeably better judgment. In Claude Code, it asks the right questions, catches its own mistakes, pushes back when a plan isn’t sound, and builds up confidence around complex, multi-service explorations before making big changes. It’s a great model to build with.” This is an attributed tester testimonial published by Anthropic, not an independent comparative evaluation of the three models.
The available official materials provide vendor benchmark results and product-specific governance information, but not a neutral cross-vendor user study or an independent test of all three models in one identical coding environment. Treat vendor scores and testimonials according to their source, and base a final decision on reproducible work in the workflow and contract you plan to use.
Quick Recap
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