Which 2026 AI Model Should You Use: GPT-5.5, Claude Opus 4.7, DeepSeek V4, or Kimi K2.6?

That table is a routing guide, not a universal ranking. The available sources do not provide one independent evaluation that tests all four models under identical prompts, tools, sampling settings, latency limits, and cost accounting. For production decisions, the better metric is cost per successful task at your quality bar.

GPT-5.5: best first test for OpenAI-centered teams

GPT-5.5 is the natural first model to evaluate if your product already uses OpenAI infrastructure. OpenAI maintains an API model page for GPT-5.5 ^[45]. OpenAI’s launch page says GPT-5.5 was introduced on April 23, 2026, and an April 24 update says GPT-5.5 and GPT-5.5 Pro became available in the API ^[57]. The New York Times also reported OpenAI’s GPT-5.5 launch, while CNBC described GPT-5.5 as OpenAI’s latest AI model and reported that it was rolling out to paid ChatGPT and Codex subscribers ^[46][52].

The strongest source-backed positioning is around coding, computer use, and deeper research workflows. CNBC reported that GPT-5.5 was better at coding, using computers, and pursuing deeper research capabilities ^[52]. For exact API economics and context length, the clearest figures in the provided source set come from secondary listings: OpenRouter lists GPT-5.5 with a 1,050,000-token context window and pricing of $5 per 1M input tokens and $30 per 1M output tokens ^[48]. The Decoder likewise reported a 1M-token API context window and $5/$30 per 1M input/output token pricing ^[58].

Because those pricing and context figures are secondary-source details, teams should verify current terms directly with OpenAI before committing to a large deployment.

Use GPT-5.5 when: you want a high-end closed model for reasoning, coding, research, document work, or computer-use workflows, and OpenAI platform fit matters as much as headline token price.

Claude Opus 4.7: strongest documented 1M-context production choice

Claude Opus 4.7 has the clearest official long-context documentation in this comparison. Anthropic says Opus 4.7 provides a 1M-token context window at standard API pricing with no long-context premium ^[1]. Anthropic’s pricing page also says Opus 4.7 includes the full 1M-token context window at standard pricing and that a 900K-token request is billed at the same per-token rate as a 9K-token request ^[2].

Anthropic positions Claude Opus 4.7 as a hybrid reasoning model for coding and AI agents with a 1M context window ^[4]. Anthropic’s product page also says Opus 4.7 brings stronger performance across coding, vision, complex multi-step tasks, and professional knowledge work ^[4].

For token pricing, OpenRouter lists Claude Opus 4.7 at $5 per 1M input tokens and $25 per 1M output tokens with a 1,000,000-token context window ^[3]. Vellum also reports $5/$25 per 1M input/output tokens and frames Opus 4.7 as a model for production coding agents and long-running workflows ^[6]. Treat Anthropic’s own docs as the source of record for policy and pricing structure, while using secondary listings as useful market checks ^[2][3][6].

Use Claude Opus 4.7 when: your system depends on long documents, large codebases, professional knowledge work, multi-step tool use, or asynchronous agents where 1M-token context economics are central.

DeepSeek V4: low-cost long-context potential, but still a preview

DeepSeek V4 is compelling for teams that care about long context and token cost. DeepSeek’s official docs list a DeepSeek-V4 Preview Release dated 2026/04/24 ^[25]. Its models and pricing page lists 1M context length, 384K maximum output, JSON output, tool calls, chat prefix completion, and FIM completion in non-thinking mode ^[30].

The same DeepSeek pricing page lists V4 input pricing by cache status and tier: cache-hit input pricing of $0.028 and $0.145 per 1M tokens, cache-miss input pricing of $0.14 and $1.74 per 1M tokens, and output pricing of $0.28 and $3.48 per 1M tokens across the shown V4 tiers ^[30]. It also says the legacy model names deepseek-chat and deepseek-reasoner will map to non-thinking and thinking modes of deepseek-v4-flash for compatibility ^[30].

The main caution is release maturity. A preview can be useful for controlled internal workloads, but production teams should test reliability, latency, structured output, tool-call behavior, refusal behavior, and regression risk before relying on it.

Use DeepSeek V4 when: cost per successful task is a top constraint, your workload benefits from 1M context, and you can run a controlled validation before production rollout.

Kimi K2.6: the open-weight multimodal contender

Kimi K2.6 is the model to evaluate when open weights and deployment flexibility matter. Artificial Analysis describes Kimi K2.6 as an open-weights model released in April 2026 with text, image, and video input, text output, and a 256K-token context window ^[70]. Artificial Analysis also says Kimi K2.6 supports image and video input natively and that its maximum context length remains 256K ^[75].

Provider listings show a roughly 256K to 262K context range, but price depends on the route. OpenRouter lists Kimi K2.6 as released on April 20, 2026, with a 262,144-token context window and pricing of $0.60 per 1M input tokens and $2.80 per 1M output tokens ^[77]. Requesty lists kimi-k2.6 at 262K context with $0.95 per 1M input tokens and $4.00 per 1M output tokens, and AI SDK lists the same $0.95/$4.00 pricing ^[76][84].

The Hugging Face page for moonshotai/Kimi-K2.6 includes benchmark tables covering OSWorld-Verified, Terminal-Bench 2.0, SWE-Bench Pro, SWE-Bench Verified, LiveCodeBench, HLE-Full, AIME 2026, and other tests ^[78]. Those benchmark tables are useful for screening, but they should not replace your own evaluation because prompts, harnesses, model settings, providers, and latency constraints can change real-world results.

Use Kimi K2.6 when: open weights, multimodal input, coding workflows, or deployment flexibility are more important than relying on the most mature closed-model enterprise stack.

Pricing and context: the practical comparison

For long-context systems, the cheapest token is not always the cheapest answer. A model with lower published pricing can still cost more if it needs more retries, drops key details in long prompts, produces invalid JSON, or requires more human review.

Why public benchmarks cannot settle this

Public benchmarks are useful for shortlisting, but they do not answer the buying question by themselves. The source set includes official model pages and pricing docs, news coverage, API aggregators, and benchmark tables for Kimi K2.6 ^{[1][30][45][48][52][70][78]}. It does not include one shared independent test of GPT-5.5, Claude Opus 4.7, DeepSeek V4, and Kimi K2.6 under identical conditions.

That matters because small evaluation choices can change the apparent winner. Prompt format, context length, allowed tools, timeout, temperature, response budget, scoring rubric, and provider infrastructure all affect results. The right enterprise metric is not leaderboard rank; it is accepted outputs per dollar at your required accuracy and review standard.

A simple benchmark plan before choosing

Run each model on work that looks like your real workload. Keep prompts, context, tools, timeouts, and scoring rules consistent.

Test at least five task types:

1. Coding: debugging, refactoring, code generation, and repo-level reasoning.
2. Long context: contracts, transcripts, research packets, policy manuals, or large codebases.
3. Structured extraction: strict JSON, schema completion, or database-ready fields.
4. Tool use: browser, code execution, internal APIs, databases, or workflow automation.
5. Domain work: finance, legal, healthcare, sales engineering, support, product analysis, or another function where your team can judge correctness.

Score each model on accuracy, source faithfulness, long-context retention, tool-call correctness, structured-output validity, latency, retry rate, safety behavior, human review time, and total cost per accepted answer.

Bottom line

Pick GPT-5.5 first if you want the strongest OpenAI-centered default for high-value reasoning, coding, research, and computer-use workflows, while verifying current API pricing and context directly with OpenAI ^{[45][57][52][48][58]}. Pick Claude Opus 4.7 first if your priority is long-context production work with clear official documentation for 1M-token context at standard pricing ^[1][2][4]. Put DeepSeek V4 into evaluation if budget and 1M context matter, but treat it as a preview until it passes your reliability tests ^[25][30]. Test Kimi K2.6 if open weights, multimodal input, and coding experimentation are key requirements, while checking provider-specific pricing and serving behavior ^{[70][75][76][77][84]}.

The strongest model is the one that wins your real tasks at the lowest reliable cost.