DeepSeek V4 vs. Kimi K2.6 vs. Claude Opus 4.7 vs. GPT-5.5: which model wins where?
There is no clean all around winner: GPT 5.5 has official OpenAI scores of 82.7% on Terminal Bench 2.0 and 58.6% on SWE Bench Pro, while Claude Opus 4.7 leads several third party coding figures [24][4]. For coding fixes, the cited SWE Bench and CursorBench numbers favor Claude Opus 4.7; for terminal agents and compu...
DeepSeek V4 vsBenchmark-Vergleiche zwischen Frontier-Modellen sind nur sinnvoll, wenn offizielle Werte, Sekundärdaten und eigene Evals getrennt betrachtet werden.
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Create a landscape editorial hero image for this Studio Global article: DeepSeek V4 vs. Kimi K2.6 vs. Claude Opus 4.7 vs. GPT-5.5: Benchmark-Vergleich. Article summary: Es gibt keinen sauber belegten Gesamtsieger: GPT 5.5 ist offiziell mit 82,7% auf Terminal Bench 2.0 und 58,6% auf SWE Bench Pro belegt, während Claude Opus 4.7 in Sekundärdaten bei SWE bench stärker wirkt; für Kimi K2.... Topic tags: ai, llm, ai benchmarks, coding agents, developer tools. Reference image context from search candidates: Reference image 1: visual subject "[Kimi K2 vs Claude Opus 4.7 vs GPT 5.5 Comparison](https://www.youtube.com/watch?v=M90iB4hpenI). . [](https://www.youtube.com" source context "Kimi K2 vs Claude Opus 4.7 vs GPT 5.5 Comparison - YouTube" Reference image 2: visual subject "[Kimi K2 vs Claude Opus 4.7 vs GPT 5.5 Comparison](https://www.yo
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Before naming a winner, separate three things: official vendor numbers, third-party comparisons and the tasks you actually need the model to do. In this source set, OpenAI publishes concrete GPT-5.5 results for Terminal-Bench 2.0 and SWE-Bench Pro . DeepSeek's own changelog establishes that V4-Pro and V4-Flash are available through its API, but it does not provide a four-way benchmark matrix . The most direct Claude Opus 4.7 and Kimi K2.6 comparison points used here come mainly from third-party analyses .
The short version
Coding and GitHub issue fixes: Claude Opus 4.7 looks strongest in the cited SWE-Bench, SWE-Bench Verified and CursorBench figures . OpenAI's own SWE-Bench Pro number for GPT-5.5 is 58.6% .
Terminal agents and computer use: GPT-5.5 is the clearest pick on documented numbers, including 82.7% on Terminal-Bench 2.0 in OpenAI's release .
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What is the short answer to "DeepSeek V4 vs. Kimi K2.6 vs. Claude Opus 4.7 vs. GPT-5.5: which model wins where?"?
There is no clean all around winner: GPT 5.5 has official OpenAI scores of 82.7% on Terminal Bench 2.0 and 58.6% on SWE Bench Pro, while Claude Opus 4.7 leads several third party coding figures [24][4].
What are the key points to validate first?
There is no clean all around winner: GPT 5.5 has official OpenAI scores of 82.7% on Terminal Bench 2.0 and 58.6% on SWE Bench Pro, while Claude Opus 4.7 leads several third party coding figures [24][4]. For coding fixes, the cited SWE Bench and CursorBench numbers favor Claude Opus 4.7; for terminal agents and computer use tasks, GPT 5.5 is the better supported starting point [4][24].
What should I do next in practice?
Kimi K2.6 is framed as a cost/quality choice at $0.60 input and $4.00 output per million tokens, while DeepSeek V4 Pro and V4 Flash are officially available through the DeepSeek API [6][25].
Budget-conscious coding agents: Kimi K2.6 is described by CodeRouter as the cost/quality winner at $0.60 input and $4.00 output per million tokens .
DeepSeek V4: V4-Pro and V4-Flash are available through the DeepSeek API, but the sources used here do not contain an official four-model benchmark table against Kimi K2.6, Claude Opus 4.7 and GPT-5.5 .
What can be compared reliably
OpenAI describes Terminal-Bench 2.0 as a benchmark for complex command-line workflows that require planning, iteration and tool coordination; GPT-5.5 reaches 82.7% there, according to OpenAI . On SWE-Bench Pro, which evaluates real-world GitHub issue resolution, OpenAI lists GPT-5.5 at 58.6% .
For DeepSeek V4, the official evidence is about availability rather than benchmark dominance. DeepSeek says V4-Pro and V4-Flash can be used through both the OpenAI ChatCompletions interface and the Anthropic interface, with the model parameters deepseek-v4-pro and deepseek-v4-flash.
For Claude Opus 4.7 and Kimi K2.6, the most useful direct comparisons in this source set are less official: LushBinary provides concrete Claude-vs.-GPT benchmark figures, while CodeRouter provides pricing and positioning claims for Kimi K2.6 and DeepSeek V4 .
Benchmark table: the numbers in the available sources
N/A means the provided sources do not include a sufficiently comparable figure for that model-benchmark combination.
Benchmark or criterion
DeepSeek V4
Kimi K2.6
Claude Opus 4.7
GPT-5.5
SWE-Bench Pro
N/A
reported by CodeRouter as tied with GPT-5.5
64.3%
58.6%
SWE-Bench Verified
N/A
N/A
87.6%
about 85%
Terminal-Bench 2.0
N/A
N/A
about 72%
82.7%
GDPval / knowledge work
N/A
N/A
about 78%
84.9%
OSWorld-Verified / computer use
N/A
N/A
about 65%
78.7%
GPQA Diamond
N/A
N/A
94.2%
about 93%
CursorBench
N/A
N/A
70%
about 65%
Tau2-bench Telecom
N/A
N/A
about 90%
98.0%
Vision & Document Arena
N/A
N/A
No. 1 in reported Arena result
N/A
Pricing/context note
V4 Flash: $0.14 input / $0.28 output per million tokens and 1M context
$0.60 input / $4.00 output per million tokens
N/A
N/A
Coding: Claude has the stronger cited scores; Kimi is the budget test
If coding benchmarks are your north star, Claude Opus 4.7 has the strongest cited case. LushBinary gives Claude Opus 4.7 64.3% on SWE-Bench Pro versus GPT-5.5's 58.6%, and OpenAI independently reports the GPT-5.5 SWE-Bench Pro figure as 58.6% . The same third-party table puts Claude ahead on SWE-Bench Verified and CursorBench .
Kimi K2.6 is still worth a look for engineering teams running many agent attempts, drafts or retries. CodeRouter says Kimi K2.6 performs at GPT-5.5 level on SWE-Bench Pro while listing much lower token prices for Kimi . That is not a substitute for your own evaluation, but it is a strong reason to include Kimi in a cost-per-accepted-fix test.
For DeepSeek V4, the official DeepSeek evidence in this source set supports availability, not a coding score: V4-Pro and V4-Flash can be called through the current API surfaces .
Terminal agents and computer use: GPT-5.5 is best documented
For terminal-heavy agents, GPT-5.5 has the clearest public support. OpenAI says Terminal-Bench 2.0 tests complex command-line workflows requiring planning, iteration and tool coordination; GPT-5.5 reaches 82.7% . LushBinary places Claude Opus 4.7 in the same benchmark at about 72% .
On the cited knowledge-work and computer-use figures, GPT-5.5 also leads Claude: 84.9% versus about 78% on GDPval, and 78.7% versus about 65% on OSWorld-Verified . That makes GPT-5.5 the strongest documented starting point for shell-heavy tool orchestration and GUI-adjacent workflows.
Vision and documents: Claude has the clearest positive signal
The available sources do not provide a complete four-model vision table. The strongest positive signal is for Claude Opus 4.7: Latent Space/AINews cites an Arena report placing Claude Opus 4.7 at No. 1 in the Vision & Document Arena .
LLM Stats also reports that Claude Opus 4.7 can process images up to 2,576 pixels on the long edge, or roughly 3.75 megapixels, while GPT-5.5 supports image input and is listed with MMMU Pro scores of 81.2% without tools and 83.2% with tools . Useful context, yes; a full four-way comparison with Kimi K2.6 and DeepSeek V4, no.
Price-performance: include Kimi K2.6 and DeepSeek V4 Flash in your own eval
Kimi K2.6 has the clearest price-performance argument in the provided sources. CodeRouter describes it as the cost/quality winner and lists $0.60 input and $4.00 output per million tokens .
DeepSeek V4 Flash is even cheaper in that same source, listed at $0.14 input and $0.28 output per million tokens with a 1M context window . DeepSeek's changelog separately confirms V4-Pro and V4-Flash support via the OpenAI ChatCompletions interface and the Anthropic interface, using deepseek-v4-pro and deepseek-v4-flash.
But price-performance is not the same as benchmark leadership. A cheaper model may be excellent for low-risk drafts, batch attempts and fallback routes; in production, the meaningful metric is cost per accepted result after retries, review time and failure handling.
How to test the four models fairly
Do not make a production decision from a single public leaderboard. Build a compact eval set from your real repositories, documents, support tickets or agent workflows. Measure not only the first answer, but also:
accepted-result rate;
retries needed;
latency;
total token cost;
severity of wrong answers;
human review time.
Also keep official and secondary data in separate columns. In this comparison, GPT-5.5 has official OpenAI numbers for Terminal-Bench 2.0 and SWE-Bench Pro . DeepSeek V4 has official API availability evidence . The strongest direct Claude and Kimi comparison claims here are from third-party sources .
Bottom line
There is no universal benchmark winner. Claude Opus 4.7 leads the cited coding-side numbers, GPT-5.5 is best documented for terminal and computer-use workflows, Kimi K2.6 has the strongest cost/quality narrative, and DeepSeek V4 is an available API candidate that still needs workload-specific evaluation before you treat it as a benchmark leader .
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