GPT-5.5 vs Claude Opus 4.7 vs DeepSeek V4 vs Kimi K2.6: Benchmark Winners by Task

Quick answer: the likely first model to test

• Terminal-based coding agents: Start with GPT-5.5. OpenAI reports 82.7% on Terminal-Bench 2.0, while public comparison tables put Claude Opus 4.7 at 69.4% and Kimi K2.6 at 66.7% .
• Real GitHub issue fixing and code repair: Start with Claude Opus 4.7. It is reported at 64.3% on SWE-Bench Pro and 87.6% on SWE-Bench Verified, ahead of GPT-5.5 on the SWE-Bench Pro figure cited here .
• Long multimodal inputs: Put Kimi K2.6 on the shortlist. It is described as supporting text, image and video input, with a 256k context route .
• Cost-sensitive high-volume API use: Test DeepSeek V4, but with guardrails. Mashable lists DeepSeek V4 at $1.74 per 1 million input tokens and $3.48 per 1 million output tokens, below GPT-5.5 at $5 and $30 and Claude Opus 4.7 at $5 and $25 .

Benchmark comparison table

A dash means the reviewed public sources do not provide a directly comparable number for that model on that benchmark. It does not mean the model cannot perform the task.

GPT-5.5: strongest public case for terminal agents

OpenAI says GPT-5.5 reaches 82.7% on Terminal-Bench 2.0 and 58.6% on SWE-Bench Pro . OpenAI describes Terminal-Bench 2.0 as testing complex command-line workflows that require planning, iteration and tool coordination, while SWE-Bench Pro evaluates real-world GitHub issue resolution .

That makes GPT-5.5 the most obvious first test for workflows such as sandboxed terminal sessions, shell-command loops, CI reproduction, file edits and agentic coding tasks that unfold over many steps. The caveat is important: on SWE-Bench Pro, Claude Opus 4.7’s reported 64.3% is higher than GPT-5.5’s 58.6%, so GPT-5.5 should not be treated as the universal coding winner .

Claude Opus 4.7: strongest public case for code repair

Claude Opus 4.7 is reported at 64.3% on SWE-Bench Pro and 87.6% on SWE-Bench Verified . DataCamp says Opus 4.7 was evaluated across 14 benchmarks covering coding, reasoning, tool use, computer use and visual reasoning .

In the shared GPT-5.5 comparison set, Claude also edges ahead on GPQA Diamond, 94.2% versus 93.6%, and MCP Atlas, 79.1% versus 75.3% . GPT-5.5, meanwhile, leads on Terminal-Bench 2.0 and BrowseComp . A practical way to read that split: Claude Opus 4.7 is the stronger first candidate for issue fixing, code repair, review-style work and tool-connected workflows; GPT-5.5 is the stronger first candidate when the job looks like a long terminal session.

Kimi K2.6: compelling for long multimodal context, but compare carefully

Kimi K2.6 is listed at 58.6% on SWE-Bench Pro and 80.2% on SWE-Bench Verified, with another guide listing 66.7% on Terminal-Bench 2.0 and 54.0% on HLE with tools . The same Kimi guide says the K2.6 figures come from Moonshot AI’s official model card and flags the SWE-Bench Pro result as using a Moonshot in-house harness .

That means Kimi K2.6’s 58.6% on SWE-Bench Pro should not be read as a perfectly apples-to-apples tie with GPT-5.5’s 58.6% unless the evaluation setup is confirmed to be the same . Kimi’s clearer product angle is context and modality: it is described as supporting text, image and video input, plus a 256k context route . If your workload involves long documents, screenshots, diagrams or video-derived context, Kimi K2.6 deserves a separate test rather than a simple leaderboard ranking.

DeepSeek V4: price is the headline, validation is the work

DeepSeek V4 is harder to place in the same benchmark table because the reviewed sources do not provide directly comparable Terminal-Bench, SWE-Bench Pro, SWE-Bench Verified or GPQA Diamond figures. The public evidence instead emphasizes other dimensions. Artificial Analysis reports that DeepSeek V4 Pro Max scored -10 on AA-Omniscience, an 11-point improvement over V3.2, while V4 Flash Max scored -23 . The same analysis reports hallucination rates of 94% for V4 Pro and 96% for V4 Flash, interpreting that as a tendency to answer even when the model does not know .

There are still reasons to test it. DataCamp describes DeepSeek V4 as using a Mixture of Experts architecture, with the Pro model containing 1.6 trillion total parameters and 49 billion active parameters, and the Flash model containing 284 billion total parameters and 13 billion active parameters . Mashable’s API pricing summary also makes DeepSeek V4 look far cheaper than GPT-5.5 and Claude Opus 4.7 on token cost .

The right takeaway is not that DeepSeek V4 wins on capability. It is that DeepSeek V4 may be attractive for high-volume, cost-sensitive or internally verifiable workflows, especially where teams can add their own evaluation, post-processing and failure detection .

Choosing by use case

Why the overall ranking is still unsettled

First, the four models have not all been evaluated in the reviewed sources with the same prompts, tools, reasoning budgets and graders. GPT-5.5 and Claude Opus 4.7 have more shared public comparison data, while Kimi K2.6 mixes model-card and in-house harness numbers, and DeepSeek V4 has gaps on the common rows .

Second, even the same benchmark name can hide differences in execution. One GPT-5.5 versus Claude Opus 4.7 analysis says the numbers are comparable in shape, not necessarily in methodology . Anthropic also notes that its Terminal-Bench 2.0 evaluation used the Terminus-2 harness with thinking disabled and specified resource allocation conditions .

Third, benchmark scores are only part of production quality. ExplainX warns that leaderboard definitions, prompts and tool policies can move scores over time and should be treated as a snapshot, not a replacement for your own evaluation harness . In practice, teams should also measure latency, cost, tool-call reliability, failure modes, hallucination behavior, security constraints and how reproducible the model’s outputs are on their own tasks.

Bottom line

Based on the public evidence here, the most defensible shortlist is: GPT-5.5 for terminal-heavy agentic coding, Claude Opus 4.7 for SWE-Bench-style code repair, Kimi K2.6 for long multimodal context, and DeepSeek V4 for cost-sensitive volume workloads that can be independently checked .

The overall champion is still unresolved. Treat the public benchmarks as a map of where to test first, not as a final procurement decision .