Kimi K2.6, DeepSeek V4, GPT-5.5 or Claude Opus 4.7: which should you choose?
Claude Opus 4.7 is the first model to test when quality matters most: VentureBeat has it ahead of GPT 5.5 and DeepSeek V4 on HLE, and CodeRouter gives it 64.3% on SWE Bench Pro [3][16]. GPT 5.5’s standout number is Terminal Bench 2.0 at 82.7%, while Kimi K2.6 ties GPT 5.5 at 58.6% on SWE Bench Pro and is listed at $...
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Claude Opus 4.7 is the first model to test when quality matters most: VentureBeat has it ahead of GPT 5.5 and DeepSeek V4 on HLE, and CodeRouter gives it 64.3% on SWE Bench Pro [3][16].
GPT 5.5’s standout number is Terminal Bench 2.0 at 82.7%, while Kimi K2.6 ties GPT 5.5 at 58.6% on SWE Bench Pro and is listed at $0.60/$4.00 per 1M input/output tokens [3][16].
DeepSeek V4 Pro and V4 Flash are the budget long context options: V4 Pro is listed at $1.74/$3.48 with 1M context, while V4 Flash is $0.14/$0.28 with 1M context, but Flash is a separate variant [4][16].
Kimi K2.6 vs DeepSeek V4 vs GPT-5.5 vs Claude Opus 4.7: benchmarks, precio y mejor usoIlustración editorial generada para representar una comparativa de modelos de IA; no contiene resultados reales de benchmark.
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Create a landscape editorial hero image for this Studio Global article: Kimi K2.6 vs DeepSeek V4 vs GPT-5.5 vs Claude Opus 4.7: benchmarks, precio y mejor uso. Article summary: Claude Opus 4.7 es la apuesta de máxima calidad en las cifras comparables: 46,9%/54,7% en HLE y 64,3% en SWE Bench Pro, pero los benchmarks mezclan modos y conviene validarlo con tus propios prompts [3][16].. Topic tags: ai, llm benchmarks, openai, anthropic, deepseek. Reference image context from search candidates: Reference image 1: visual subject "[Sign in](https://medium.com/m/signin?operation=login&redirect=https%3A%2F%2Fmedium.com%2F%40cognidownunder%2Fclaude-opus-4-7-leads-on-code-gpt-5-5-wins-intelligence-and-kimi-k2-6-" source context "Claude Opus 4.7 Leads on Code, GPT 5.5 Wins Intelligence, and ..." Reference image 2: visual subject "[Sign in](https://medium.com/m/signin?operation=login&redirect=https%3
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The useful reading of the available benchmarks is not that one model wins everything. It is that the models split into four practical starting points: Claude Opus 4.7 when quality matters more than spend, GPT-5.5 when Terminal-Bench and OpenAI/ChatGPT/Codex workflows matter, Kimi K2.6 when you want low-cost competitive coding, and DeepSeek V4 when you need many long-context calls at lower prices . Treat the figures as directional, not absolute: the public data mixes variants and settings such as tools, no tools, high effort, max effort and thinking modes .
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What is the short answer to "Kimi K2.6, DeepSeek V4, GPT-5.5 or Claude Opus 4.7: which should you choose?"?
Claude Opus 4.7 is the first model to test when quality matters most: VentureBeat has it ahead of GPT 5.5 and DeepSeek V4 on HLE, and CodeRouter gives it 64.3% on SWE Bench Pro [3][16].
What are the key points to validate first?
Claude Opus 4.7 is the first model to test when quality matters most: VentureBeat has it ahead of GPT 5.5 and DeepSeek V4 on HLE, and CodeRouter gives it 64.3% on SWE Bench Pro [3][16]. GPT 5.5’s standout number is Terminal Bench 2.0 at 82.7%, while Kimi K2.6 ties GPT 5.5 at 58.6% on SWE Bench Pro and is listed at $0.60/$4.00 per 1M input/output tokens [3][16].
What should I do next in practice?
DeepSeek V4 Pro and V4 Flash are the budget long context options: V4 Pro is listed at $1.74/$3.48 with 1M context, while V4 Flash is $0.14/$0.28 with 1M context, but Flash is a separate variant [4][16].
It leads the comparable HLE figures against GPT-5.5 and DeepSeek, and CodeRouter puts it first on SWE-Bench Pro at 64.3% .
Terminal work, agents and the OpenAI stack
GPT-5.5
VentureBeat reports 82.7% on Terminal-Bench 2.0, ahead of Claude Opus 4.7 and DeepSeek V4; a practical guide also links it to ChatGPT/Codex workflows .
Competitive coding at a lower token price
Kimi K2.6
CodeRouter lists it at 58.6% on SWE-Bench Pro, tied with GPT-5.5, and prices it at $0.60/$4.00 per 1M input/output tokens .
High-volume calls with long context
DeepSeek V4-Pro or V4 Flash
V4-Pro is listed at $1.74/$3.48 per 1M tokens with 1M context; V4 Flash is listed at $0.14/$0.28 with 1M context, though it is a different variant .
A documented self-hosting route
Kimi K2.6
Verdent says K2.6 weights are on Hugging Face and can run with vLLM, SGLang or KTransformers .
What the benchmarks actually say
Humanity’s Last Exam, or HLE, is a multimodal academic benchmark with 2,500 questions across mathematics, humanities and natural sciences, designed to test frontier capabilities with verifiable answers . SWE-Bench Pro evaluates software engineering over multi-language, real-world GitHub issues . Terminal-Bench 2.0 appears in VentureBeat’s agentic and software-engineering results .
Benchmark
Main read
Available numbers
HLE without tools
Claude Opus 4.7 leads among the three models in VentureBeat’s comparable table.
Claude Opus 4.7: 46.9%; GPT-5.5: 41.4%; DeepSeek V4: 37.7%. Kimi K2.6 does not appear in the same comparable extract .
HLE with tools
Claude stays ahead of GPT-5.5 and DeepSeek; Kimi has a competitive figure, but from a different source.
Claude Opus 4.7: 54.7%; GPT-5.5: 52.2%; DeepSeek V4: 48.2% in VentureBeat. CodeRouter lists Kimi K2.6 at 54.0 on HLE with tools, but that is not the same table .
SWE-Bench Pro
Claude is the leader; GPT-5.5 and Kimi form the second group; DeepSeek is close but lower.
CodeRouter reports Claude Opus 4.7 at 64.3%, GPT-5.5 and Kimi K2.6 at 58.6%, and DeepSeek V4-Pro around 55%; VentureBeat cites 55.4% for DeepSeek .
Terminal-Bench 2.0
This is the clearest benchmark argument for GPT-5.5 in the comparable data.
GPT-5.5: 82.7%; Claude Opus 4.7: 69.4%; DeepSeek V4: 67.9%. No Kimi K2.6 figure appears in that VentureBeat extract .
The practical read is simple: Claude Opus 4.7 has the strongest general quality signal in the comparable figures, GPT-5.5 has a clear Terminal-Bench 2.0 advantage, Kimi K2.6 stands out for coding value, and DeepSeek V4 becomes more attractive when cost and context window are the binding constraints .
Price and context: the leaderboard does not pay the invoice
For agents that make many calls, token price can matter more than a small benchmark gap. The available sources put Kimi K2.6 and DeepSeek V4 in the aggressive-cost band, while GPT-5.5 and Claude Opus 4.7 sit in premium territory .
Model or variant
Reported price
Reported context
Note
Claude Opus 4.7
$5 input / $25 output per 1M tokens in Artificial Analysis .
1M tokens, with 128K max output tokens .
Artificial Analysis also describes it as a leading intelligence model, but expensive, slower than average and very verbose .
GPT-5.5
$5 input / $30 output per 1M tokens in CodeRouter .
1M tokens .
Best fit when you want the Terminal-Bench signal or already work in ChatGPT/Codex .
Kimi K2.6
$0.60 input / $4.00 output per 1M tokens in CodeRouter .
256K tokens .
Artificial Analysis also shows 256K context for Kimi versus 1000K for Claude Opus 4.7 in a direct comparison .
DeepSeek V4-Pro
$1.74 input / $3.48 output per 1M tokens in CodeRouter .
1M tokens .
Attractive for lower-cost volume with long context, although it does not lead HLE or SWE-Bench Pro in the available figures .
DeepSeek V4 Flash
$0.14 input / $0.28 output per 1M tokens in CodeRouter .
1M tokens .
It is a separate variant, so do not automatically transfer V4-Pro or V4-Pro-Max benchmark numbers to Flash .
The Claude line needs a double-check before budgeting: Artificial Analysis reports $5/$25 and 1M context, while CodeRouter’s Kimi review lists different Claude values . For production, use the current price and contract from the provider you will actually call.
Which model should you pick?
Choose Claude Opus 4.7 if mistakes are expensive
Claude Opus 4.7 is the sensible first trial for difficult code review, long analysis and work where finding hidden defects matters more than saving tokens. It leads GPT-5.5 and DeepSeek V4 on HLE in VentureBeat, leads SWE-Bench Pro in CodeRouter, and Artificial Analysis places it among the leading intelligence models while noting high cost, slower speed and verbosity . It also has a reported 1M context window and is available through Anthropic’s API, Amazon Bedrock, Microsoft Azure and Google Vertex, according to Artificial Analysis .
Choose GPT-5.5 if your workflow is built around OpenAI or terminal agents
GPT-5.5 does not beat Claude Opus 4.7 on HLE in the VentureBeat data, but it has the strongest Terminal-Bench 2.0 figure in the comparable set: 82.7% versus 69.4% for Claude Opus 4.7 and 67.9% for DeepSeek V4 . If your team already works in ChatGPT or Codex, a practical guide frames GPT-5.5 as the natural route to test before moving fully to another provider .
Choose Kimi K2.6 if you need competitive coding at a lower price
Kimi K2.6 has the clearest cost/performance case in the sources: CodeRouter ties it with GPT-5.5 at 58.6% on SWE-Bench Pro and lists it at $0.60/$4.00 per 1M input/output tokens . Its 256K context window is smaller than the 1M listed for GPT-5.5 and DeepSeek V4-Pro in the same table, but it may be enough if your repo, issue history and tool traces fit inside that window . If self-hosting is part of the requirement, Verdent reports K2.6 weights on Hugging Face, runnable with vLLM, SGLang or KTransformers, with 4× H100 as the minimum viable hardware for the INT4 variant at reduced context .
Choose DeepSeek V4 if long-context volume is the bottleneck
DeepSeek V4 Pro/Pro-Max trails Claude Opus 4.7 and GPT-5.5 on HLE, Terminal-Bench 2.0 and SWE-Bench Pro in the VentureBeat figures, but its price/context profile makes it worth testing for high-volume pipelines . If minimum cost is the goal, CodeRouter lists V4 Flash even lower, at $0.14/$0.28 per 1M input/output tokens with 1M context; treat Flash as a separate variant rather than a drop-in benchmark proxy for V4-Pro or V4-Pro-Max .
Before you migrate, check these four things
The setup is not identical. HLE appears with and without tools, and other sources use high effort, max effort or thinking modes .
Variants are not interchangeable. GPT-5.5 is not GPT-5.5 Pro; DeepSeek V4-Pro, V4-Pro-Max and V4 Flash should not be treated as the same model .
Benchmarks and prices age quickly. Verdent warns that numbers can go stale quickly in a fast release cycle .
Your own task matters more than a leaderboard. A practical guide recommends running the same task before changing routes, instead of choosing the model with the loudest launch .
Bottom line
If quality is the only thing that matters, start with Claude Opus 4.7. If terminal work, agents or OpenAI continuity matter more, test GPT-5.5. If you want competitive coding at a much lower token price, benchmark Kimi K2.6. If the bottleneck is cheap high-volume long-context usage, validate DeepSeek V4-Pro or V4 Flash, while treating Flash as a separate variant .
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