GPT‑5.5 vs Claude Opus 4.7 vs Kimi K2.6 vs DeepSeek V4: the 2026 benchmark guide

Before reading the table: three things that matter

1. Benchmark families measure different skills. Terminal‑Bench, SWE‑Bench, BrowseComp, OSWorld, GPQA and Humanity’s Last Exam are not interchangeable. A model that is excellent at fixing repository issues may not be the best at web research, long-context recall or GUI-style computer use.
2. Tool access and inference effort can change the result. OpenAI’s system card describes GPT‑5.5 Pro as the same underlying model using a setting that makes use of parallel test-time compute, so GPT‑5.5 and GPT‑5.5 Pro numbers should not be read as the same compute budget.
3. Public benchmarks are for shortlisting, not final buying decisions. LM Council warns that independently run benchmarks may not match self-reported scores, so teams should run internal evaluations on their own tasks before standardising on a model.

Model snapshot

Head-to-head benchmark view

Which model should you choose?

1. Terminal-heavy autonomous agents: GPT‑5.5

If your workload involves terminal actions, browser/tool use, operating-system tasks and multi-step agent loops, GPT‑5.5 is the strongest candidate in this data set. OpenAI reports 82.7% on Terminal‑Bench 2.0, 78.7% on OSWorld‑Verified, 84.4% on BrowseComp and 55.6% on Toolathlon.

GPT‑5.5 Pro’s 90.1% BrowseComp score is notable, but it should not be treated as the regular GPT‑5.5 result. OpenAI’s system card says Pro uses the same underlying model with parallel test-time compute.

Best fit: coding agents, browser research agents, computer-use automation and tool-heavy enterprise assistants.

2. Production codebase repair: Claude Opus 4.7

If the core job is fixing bugs in real repositories, preparing pull requests, passing tests and understanding large codebases, Claude Opus 4.7 should be near the top of the shortlist. Its reported 87.6% on SWE‑Bench Verified and 64.3% on SWE‑Bench Pro put it ahead on these software-engineering benchmarks.

Anthropic also describes Claude Opus 4.7 as a hybrid reasoning model for coding and AI agents with a 1M context window, which makes it a natural candidate for large-codebase workflows.

Best fit: repository maintenance, code review, complex refactors, developer copilots and engineering agents.

3. Open-weights coding stack: Kimi K2.6

If self-hosting, open weights or greater deployment control are requirements, Kimi K2.6 is one of the strongest options in this comparison. Kimi’s official table reports 66.7% on Terminal‑Bench 2.0, 58.6% on SWE‑Bench Pro, 80.2% on SWE‑Bench Verified, 52.2% on SciCode and 89.6 on LiveCodeBench v6.

Kimi’s public material also shows strong agentic/search-style signals, including 83.2% on BrowseComp and 86.3% for Agent Swarm BrowseComp. Artificial Analysis says Kimi K2.6 supports native image/video input and a 256k context length.

Best fit: open model deployments, coding agents, research agents and teams that need more hosting control.

4. Long-context open-source experimentation: DeepSeek V4

DeepSeek said V4 Preview went live and was open-sourced on 24 April 2026. The DeepSeek-V4-Pro model card presents the V4 series as Mixture-of-Experts language models.

For DeepSeek V4-Pro/Pro-Max, the reported benchmark set includes 67.9 on Terminal Bench 2.0, 80.6 on SWE Verified, 55.4 on SWE Pro and 90.1 on GPQA Diamond. That makes it a serious candidate for open-source/open-weights experimentation and long-context workloads, but the exact variant matters.

Best fit: long-context applications, open-source/open-weights experiments and teams comparing hosted frontier models with deployable alternatives.

5. Science and math reasoning: Claude leads on GPQA, but do not stop there

In the available reported numbers, Claude Opus 4.7 reaches 94.2% on GPQA Diamond. Kimi K2.6 reports 90.5 on GPQA-Diamond and 96.4 on AIME 2026. DeepSeek V4-Pro/Pro-Max reports 90.1 on GPQA Diamond.

That makes Claude the strongest science-reasoning shortlist candidate on GPQA Diamond in this source set, but math and science workloads should not be decided on a single benchmark. Tool access, effort mode and benchmark setup can all affect outcomes.

Practical evaluation checklist

• Do not buy from one leaderboard. Public and self-reported scores can diverge from independent runs, so test the models on your own workload with the same prompts, tool budget, timeout and scoring rubric.
• Track GPT‑5.5 and GPT‑5.5 Pro separately. Pro uses parallel test-time compute, so regular and Pro results should not be treated as the same compute budget.
• Define your open-weights requirement up front. If data control, self-hosting or model customisation is mandatory, evaluate Kimi K2.6 and DeepSeek V4 in a separate lane from fully hosted proprietary models.
• Do not judge long context by window size alone. Claude Opus 4.7 is positioned with a 1M context window, Kimi K2.6 is reported with a 256k maximum context length, and DeepSeek V4 materials position it for long-context use; still, you should test recall, instruction following and cost on your own documents.
• For coding agents, run public benchmarks and internal repo tests. SWE‑Bench-style results are useful, but production repositories add dependency issues, flaky tests, code-style rules and review constraints.

Limitations

• This source set does not provide a complete public comparison in which all four models are evaluated by the same independent lab, using the same harness, tool access and effort setting. LM Council also warns that independent and self-reported benchmark results may not match.
• GPT‑5.5 Pro and GPT‑5.5 should not be collapsed into one result, because OpenAI describes Pro as the same underlying model using parallel test-time compute.
• DeepSeek V4 scores are variant-specific. V4 Preview, V4-Pro and Pro-Max-style naming should not be merged into a single generic “DeepSeek V4” score.
• Open-weights deployments such as Kimi K2.6 and DeepSeek V4 can behave differently depending on serving stack, hardware, quantisation and context settings, so published benchmarks should be paired with deployment-specific evaluation.

Bottom line

Shortlist GPT‑5.5 when the product depends on agentic computer use, browsing, tool orchestration and terminal-heavy coding.

Prioritise Claude Opus 4.7 when the core value is repo-level bug fixing, codebase repair and SWE‑Bench-style software engineering.

Evaluate Kimi K2.6 when you need an open-weights coding model with strong SWE‑Bench, Terminal‑Bench and agentic search signals.

Shortlist DeepSeek V4-Pro/Pro-Max when long-context open-source/open-weights experimentation and deployability are key constraints, while verifying the exact variant and benchmark setup.

The safest decision is to use public benchmarks to build a shortlist, then choose the final model on your own tasks, latency, cost, privacy constraints and failure-mode tests.