There is no proven universal best LLM for coding among Claude Opus 4.8, GPT-5.5 and Gemini 3.1 Pro. The available scores are vendor-published snapshots from different dates and evaluation setups, not a single independent head-to-head test. Choose by the work your team needs done, then evaluate the model inside the coding agent, repository and governance environment you intend to use. This comparison reflects official material available through October 7, 2026.
What the published coding benchmarks show
Anthropic’s May 2026 Claude Opus 4.8 System Card publishes the three-model comparisons below. These are Anthropic-reported results; the card’s figures should be read as evidence for particular tasks and setups, not as a universal ranking.
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| Benchmark | Claude Opus 4.8 | GPT-5.5 | Gemini 3.1 Pro | What to note |
|---|---|---|---|---|
| SWE-bench Pro | 69.2% | 58.6% | 54.2% | Results published in Anthropic’s May 2026 System Card. |
| Terminal-Bench 2.1 | 74.6% | 78.2% | 70.3% | Results published in Anthropic’s May 2026 System Card. Anthropic also notes 83.4% for GPT-5.5 with the Codex CLI harness; that is a different harness and should not be treated as the same run. |
| BrowseComp | 84.3% single-agent; 88.5% multi-agent | 84.4% | 85.9% | Results published in Anthropic’s May 2026 System Card. Opus has separate single- and multi-agent figures; do not assume the other figures use an equivalent agent configuration. |
| OSWorld-Verified | 83.4% | 78.7% | 76.2% | Results published in Anthropic’s May 2026 System Card. |
Anthropic’s May 2026 System Card also reports 88.6% for Claude Opus 4.8 on SWE-bench Verified. That figure is not a three-way comparison in the reported details here. It should not be compared directly with Gemini 3.1 Pro’s separate SWE-bench Verified result below as if both came from a common test run.
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Google DeepMind’s Gemini 3.1 Pro Model Card reports its results as of February 2026 under its stated modes and evaluation setup:
#1 Best Overall
- SWE-bench Verified: 80.6% on a single attempt.
- SWE-bench Pro (Public): 54.2% on a single attempt.
- Terminal-Bench 2.0: 68.5% using the Terminus-2 harness.
- MRCR v2: 84.9% average at 128k and 26.3% pointwise at 1M. The card notes that some compared models do not support the 1M evaluation.
The two cards differ in benchmark version, harness, prompting, inference settings, model versions and reporting date. For example, Google’s Terminal-Bench 2.0 result with Terminus-2 is not interchangeable with the Terminal-Bench 2.1 figures in Anthropic’s table. Treat each score as a dated result for its stated setup, not as a normalized cross-vendor comparison.
Choose for the engineering task, not the headline score
A benchmark can help identify what to test, but your result depends on the job, the agent’s tools and the operating environment. A model that performs well on issue resolution may not be the best fit for code review, terminal operation or a long-context repository investigation. Before choosing, define the work and evaluate the candidate in the actual IDE or agent workflow, with your repository and policies.
Rank #2
- Repository maintenance and issue resolution: test whether the model can understand the relevant files, make a focused change, run the right checks and recover when those checks fail.
- Terminal work: evaluate the complete agent-and-harness setup, including command execution, permissions, tool feedback and how it handles errors. Keep harness-specific results separate.
- Code generation and review: use representative tasks to assess correctness, compatibility with your conventions, test quality and whether review catches defects rather than merely producing plausible explanations.
- Long-context repository analysis: check how the model handles the context sizes and retrieval path your workload actually requires. A model-card result at a particular context length is not a guarantee for every repository or product configuration.
Anthropic’s May 28, 2026 announcement includes this 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 a named tester testimonial republished by Anthropic, not an independent comparative evaluation.
Run a representative evaluation before selecting a model
- Define the task set. Select real examples of the work you expect the model to perform: maintenance issues, terminal tasks, code reviews or repository analysis. Use tasks your team can judge consistently.
- Match the deployment. Test each candidate through the intended IDE or agent, harness, tools, permissions and model configuration. A score from another harness does not predict an identical result in yours.
- Set evaluation rules. Decide in advance how you will measure correctness, successful tests, regressions, human intervention and safe handling of tool access. Keep the setup consistent across candidates.
- Measure operational fit. Include cost and latency for the exact service configurations you are considering. The cited material does not establish an aligned three-way cost or latency comparison.
- Review failure cases. Inspect incorrect edits, missed requirements, unsafe commands and unhelpful tool use, not just task completion rates. Decide whether review and rollback controls are adequate for the consequences of a mistake.
Enterprise governance depends on the product and contract
The model name alone does not establish whether a deployment meets an organization’s requirements. Confirm controls for the exact product, plan, API path and contract. A practical review should cover:
- Identity and administration: SSO, user provisioning, role management and access to audit events.
- Data handling: retention, use of submitted data for training, logging and deletion terms for the selected tier.
- Encryption and residency: key-management options and where inference and stored data are processed.
- Compliance scope: applicable certifications, contractual commitments and eligibility requirements for the organization and workload.
- Cost controls: included usage versus separately billed tokens, plus administrator limits and spending controls.
- Repository and tool access: connector permissions, agent execution boundaries, logging and human review.
What the reviewed Anthropic Enterprise material specifies
Anthropic’s Enterprise help material, 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 plan described there, Enterprise usage is billed separately at standard API rates. Confirm that these terms apply to the specific offering and contract being considered.
What the reviewed Google and OpenAI material establishes
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 readers to applicable service terms. That model card does not establish a complete comparison of Google’s enterprise administration, data-handling, residency or contract controls.
OpenAI’s GPT-5.5 System Card, dated April 23, 2026, notes an April 24 update and an August 19 correction to one reported safety-evaluation figure. It describes predeployment safety evaluations, Preparedness Framework evaluations, red-teaming and deployment safeguards. It does not by itself establish the full enterprise controls or contract terms for every GPT-5.5 access path. Verify the product and applicable terms directly before procurement.
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Use the published results to form a shortlist, not to declare a winner. The evidence reviewed here does not include a neutral, common-harness coding study of all three models or a complete contract-level governance matrix across providers. The defensible choice is the model and deployment that perform well on your representative engineering tasks and satisfy your organization’s specific access, data, compliance and cost requirements.
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