The wrong move is to ask only which model is best. The better question is: what are you paying it to do?

Quick recommendation

Why one leaderboard is not enough

There is no public, fully apples-to-apples benchmark that covers all four models under the same evaluator, date, reasoning budget, tool access and routing setup. The available evidence comes from vendor pages, third-party leaderboards, media summaries, API documentation, model-routing pages and individual tests, and those sources do not all measure the same thing.

That matters because settings can change the outcome. Artificial Analysis separates GPT-5.5 xHigh, GPT-5.5 High and Claude Opus 4.7 Adaptive Reasoning Max Effort, while OpenAI’s API documentation lists GPT-5.5 reasoning effort options from none through xhigh. A model that wins on one public benchmark may not win inside your prompts, tools, latency budget or review process.

Use public benchmarks to narrow the shortlist. Use your own workload to pick the production model.

Benchmarks worth comparing

GPT-5.5: the strongest default starting point

OpenAI’s release page says GPT-5.5 and GPT-5.5 Pro were made available on April 24, 2026. OpenAI’s API documentation describes gpt-5.5 as a model for coding and professional work, with a 1M-token context window, 128K maximum output, function calling, web search, file search and computer-use tools.

The public benchmark picture makes GPT-5.5 the best first test for many high-value workflows. Artificial Analysis gives GPT-5.5 xHigh a score of 60 and GPT-5.5 High a score of 59, while Claude Opus 4.7 is listed at 57. VentureBeat reports GPT-5.5 at 82.7% on Terminal-Bench 2.0, ahead of Claude Opus 4.7 at 69.4% and DeepSeek V4 at 67.9%.

The trade-off is price. OpenAI lists GPT-5.5 at $5 per 1M input tokens and $30 per 1M output tokens. If your workflow produces long reports, loops through many tool calls or retries frequently, output cost can become the deciding factor.

Best first tests: complex coding agents, terminal automation, cross-tool research, professional workflows that combine function calling with web or file search.

Claude Opus 4.7: strong for long, careful, multi-step work

Claude Opus 4.7 is positioned around long-horizon work and disciplined output. Anthropic says it tied for the top overall score on its internal research-agent benchmark at 0.715, delivered the most consistent long-context performance among the models it tested, and scored 0.813 on the General Finance module versus 0.767 for Opus 4.6.

VentureBeat’s Humanity’s Last Exam summary also favors Claude in some reasoning settings. Without tools, Claude Opus 4.7 scored 46.9%, above GPT-5.5 at 41.4% and DeepSeek V4 at 37.7%. With tools, Claude scored 54.7%, above GPT-5.5 base at 52.2% but below GPT-5.5 Pro at 57.2%.

That does not mean Claude wins every technical metric. On Terminal-Bench 2.0, GPT-5.5’s 82.7% is well ahead of Claude Opus 4.7’s 69.4%. A separate third-party breakdown reports Claude Opus 4.7 at 82.4% on SWE-bench Verified, but that should not be mixed directly with SWE-Bench Pro or with benchmarks run under different settings.

Best first tests: long-document research, financial-document analysis, evidence-sensitive writing, multi-step analysis and workflows where disclosure, consistency and reviewability matter.

DeepSeek V4: the cost-efficiency candidate

DeepSeek V4’s standout advantage is price. Mashable reports DeepSeek V4 API pricing at $1.74 per 1M input tokens and $3.48 per 1M output tokens. In the same comparison, GPT-5.5 is listed at $5 / $30 and Claude Opus 4.7 at $5 / $25.

Performance looks near-frontier in some areas, but not broadly first in the public summaries provided. VentureBeat reports DeepSeek V4 at 37.7% on Humanity’s Last Exam without tools and 48.2% with tools, behind GPT-5.5, GPT-5.5 Pro and Claude Opus 4.7 in those rows. On Terminal-Bench 2.0, DeepSeek’s 67.9% is close to Claude Opus 4.7’s 69.4%, but far below GPT-5.5’s 82.7%.

That makes DeepSeek V4 a serious first-round candidate for cost-sensitive production systems, not an automatic replacement for every frontier model. The real test is whether it clears your quality bar often enough that its lower token price is not offset by retries, human review or slower resolution.

Best first tests: batch processing, high-throughput inference, budget-constrained applications and workflows where some review is acceptable but token cost must fall sharply.

Kimi K2.6: the open-weight and long-context option

Kimi K2.6 is most interesting where open weights, long context and multimodal input matter. Artificial Analysis calls it a new leading open-weight model and says it supports image and video input with text output, with a 256K maximum context length.

OpenRouter’s Kimi K2.6 page lists Artificial Analysis scores of 53.9 for Intelligence, 47.1 for Coding and 66.0 for Agentic, along with 256K maximum tokens and 66K maximum output tokens. On BrowseComp, DocsBot reports Kimi K2.6 at 83.2%, close to GPT-5.5 at 84.4% in the same comparison page.

The caution is comparability. Some Kimi K2.6 materials primarily compare it with GPT-5.4 or Claude Opus 4.6, rather than with GPT-5.5, Claude Opus 4.7 and DeepSeek V4 in a single shared evaluation. Treat Kimi as a high-priority candidate if its deployment and modality profile fits your needs, but validate it directly before making it the default.

Best first tests: open-weight workflows, long-context processing, image or video input, and teams looking for a balance between capability, cost and model control.

Cost and capacity: the practical comparison

API price is only part of total cost. For tool-heavy or long-running workflows, OpenAI’s GPT-5.5 guidance recommends benchmarking against other models on accuracy, token consumption and end-to-end latency. The same OpenAI model documentation also shows that GPT-5.5 reasoning effort can be adjusted from none to xhigh, which means cost and quality can shift even within the same model family.

How to run a useful bake-off

A serious model evaluation should track more than the final answer. At minimum, compare task success rate, failure type, end-to-end latency, token usage, retry rate and human-review burden. Use the same prompts, the same tools, the same permissions and the same acceptance criteria wherever possible.

Individual tests can add useful color, but they are not universal leaderboards. In an April 2026 coding test, AkitaOnRails scored Claude Opus 4.7 at 97, GPT-5.5 xHigh Codex at 96, Kimi K2.6 at 87 and DeepSeek V4 Pro at 69; the same table estimated costs of about $1.10 for Claude Opus 4.7, $10 for GPT-5.5 xHigh Codex, $0.30 for Kimi K2.6 and $0.50 for DeepSeek V4 Pro.

The value of that kind of result is not that every team should copy the ranking. It is that rankings can move when the codebase, tool permissions, prompts, review standards and retry rules change.

Bottom line

If you can only send one model into the first round of testing, start with GPT-5.5. It has the strongest public signals in the Artificial Analysis composite ranking and a clear lead in VentureBeat’s Terminal-Bench 2.0 summary.

If your workload is long-form research, finance, document analysis or careful multi-step reasoning, Claude Opus 4.7 belongs in the first tier. Anthropic’s internal research-agent data and VentureBeat’s Humanity’s Last Exam summary both support taking it seriously for those tasks.

If your main constraint is call volume and budget, DeepSeek V4 is the model to test early. Its reported input and output prices are much lower than GPT-5.5 and Claude Opus 4.7 in the Mashable comparison.

If you need open weights, multimodal input or 256K context, Kimi K2.6 is one of the most important candidates in the public evidence here, but the lack of a complete same-source four-way comparison means it needs direct validation in your own stack.

The safest conclusion is simple: use public benchmarks to decide where to begin, then let your real tasks decide what goes into production.

The wrong move is to ask only which model is best. The better question is: what are you paying it to do?

Quick recommendation

Why one leaderboard is not enough

There is no public, fully apples-to-apples benchmark that covers all four models under the same evaluator, date, reasoning budget, tool access and routing setup. The available evidence comes from vendor pages, third-party leaderboards, media summaries, API documentation, model-routing pages and individual tests, and those sources do not all measure the same thing.

That matters because settings can change the outcome. Artificial Analysis separates GPT-5.5 xHigh, GPT-5.5 High and Claude Opus 4.7 Adaptive Reasoning Max Effort, while OpenAI’s API documentation lists GPT-5.5 reasoning effort options from none through xhigh. A model that wins on one public benchmark may not win inside your prompts, tools, latency budget or review process.

Use public benchmarks to narrow the shortlist. Use your own workload to pick the production model.

Benchmarks worth comparing

GPT-5.5: the strongest default starting point

OpenAI’s release page says GPT-5.5 and GPT-5.5 Pro were made available on April 24, 2026. OpenAI’s API documentation describes gpt-5.5 as a model for coding and professional work, with a 1M-token context window, 128K maximum output, function calling, web search, file search and computer-use tools.

The public benchmark picture makes GPT-5.5 the best first test for many high-value workflows. Artificial Analysis gives GPT-5.5 xHigh a score of 60 and GPT-5.5 High a score of 59, while Claude Opus 4.7 is listed at 57. VentureBeat reports GPT-5.5 at 82.7% on Terminal-Bench 2.0, ahead of Claude Opus 4.7 at 69.4% and DeepSeek V4 at 67.9%.

The trade-off is price. OpenAI lists GPT-5.5 at $5 per 1M input tokens and $30 per 1M output tokens. If your workflow produces long reports, loops through many tool calls or retries frequently, output cost can become the deciding factor.

Best first tests: complex coding agents, terminal automation, cross-tool research, professional workflows that combine function calling with web or file search.

Claude Opus 4.7: strong for long, careful, multi-step work

Claude Opus 4.7 is positioned around long-horizon work and disciplined output. Anthropic says it tied for the top overall score on its internal research-agent benchmark at 0.715, delivered the most consistent long-context performance among the models it tested, and scored 0.813 on the General Finance module versus 0.767 for Opus 4.6.

VentureBeat’s Humanity’s Last Exam summary also favors Claude in some reasoning settings. Without tools, Claude Opus 4.7 scored 46.9%, above GPT-5.5 at 41.4% and DeepSeek V4 at 37.7%. With tools, Claude scored 54.7%, above GPT-5.5 base at 52.2% but below GPT-5.5 Pro at 57.2%.

That does not mean Claude wins every technical metric. On Terminal-Bench 2.0, GPT-5.5’s 82.7% is well ahead of Claude Opus 4.7’s 69.4%. A separate third-party breakdown reports Claude Opus 4.7 at 82.4% on SWE-bench Verified, but that should not be mixed directly with SWE-Bench Pro or with benchmarks run under different settings.

Best first tests: long-document research, financial-document analysis, evidence-sensitive writing, multi-step analysis and workflows where disclosure, consistency and reviewability matter.

DeepSeek V4: the cost-efficiency candidate

DeepSeek V4’s standout advantage is price. Mashable reports DeepSeek V4 API pricing at $1.74 per 1M input tokens and $3.48 per 1M output tokens. In the same comparison, GPT-5.5 is listed at $5 / $30 and Claude Opus 4.7 at $5 / $25.

Performance looks near-frontier in some areas, but not broadly first in the public summaries provided. VentureBeat reports DeepSeek V4 at 37.7% on Humanity’s Last Exam without tools and 48.2% with tools, behind GPT-5.5, GPT-5.5 Pro and Claude Opus 4.7 in those rows. On Terminal-Bench 2.0, DeepSeek’s 67.9% is close to Claude Opus 4.7’s 69.4%, but far below GPT-5.5’s 82.7%.

That makes DeepSeek V4 a serious first-round candidate for cost-sensitive production systems, not an automatic replacement for every frontier model. The real test is whether it clears your quality bar often enough that its lower token price is not offset by retries, human review or slower resolution.

Best first tests: batch processing, high-throughput inference, budget-constrained applications and workflows where some review is acceptable but token cost must fall sharply.

Kimi K2.6: the open-weight and long-context option

Kimi K2.6 is most interesting where open weights, long context and multimodal input matter. Artificial Analysis calls it a new leading open-weight model and says it supports image and video input with text output, with a 256K maximum context length.

OpenRouter’s Kimi K2.6 page lists Artificial Analysis scores of 53.9 for Intelligence, 47.1 for Coding and 66.0 for Agentic, along with 256K maximum tokens and 66K maximum output tokens. On BrowseComp, DocsBot reports Kimi K2.6 at 83.2%, close to GPT-5.5 at 84.4% in the same comparison page.

The caution is comparability. Some Kimi K2.6 materials primarily compare it with GPT-5.4 or Claude Opus 4.6, rather than with GPT-5.5, Claude Opus 4.7 and DeepSeek V4 in a single shared evaluation. Treat Kimi as a high-priority candidate if its deployment and modality profile fits your needs, but validate it directly before making it the default.

Best first tests: open-weight workflows, long-context processing, image or video input, and teams looking for a balance between capability, cost and model control.

Cost and capacity: the practical comparison

API price is only part of total cost. For tool-heavy or long-running workflows, OpenAI’s GPT-5.5 guidance recommends benchmarking against other models on accuracy, token consumption and end-to-end latency. The same OpenAI model documentation also shows that GPT-5.5 reasoning effort can be adjusted from none to xhigh, which means cost and quality can shift even within the same model family.

How to run a useful bake-off

A serious model evaluation should track more than the final answer. At minimum, compare task success rate, failure type, end-to-end latency, token usage, retry rate and human-review burden. Use the same prompts, the same tools, the same permissions and the same acceptance criteria wherever possible.

Individual tests can add useful color, but they are not universal leaderboards. In an April 2026 coding test, AkitaOnRails scored Claude Opus 4.7 at 97, GPT-5.5 xHigh Codex at 96, Kimi K2.6 at 87 and DeepSeek V4 Pro at 69; the same table estimated costs of about $1.10 for Claude Opus 4.7, $10 for GPT-5.5 xHigh Codex, $0.30 for Kimi K2.6 and $0.50 for DeepSeek V4 Pro.

The value of that kind of result is not that every team should copy the ranking. It is that rankings can move when the codebase, tool permissions, prompts, review standards and retry rules change.

Bottom line

If you can only send one model into the first round of testing, start with GPT-5.5. It has the strongest public signals in the Artificial Analysis composite ranking and a clear lead in VentureBeat’s Terminal-Bench 2.0 summary.

If your workload is long-form research, finance, document analysis or careful multi-step reasoning, Claude Opus 4.7 belongs in the first tier. Anthropic’s internal research-agent data and VentureBeat’s Humanity’s Last Exam summary both support taking it seriously for those tasks.

If your main constraint is call volume and budget, DeepSeek V4 is the model to test early. Its reported input and output prices are much lower than GPT-5.5 and Claude Opus 4.7 in the Mashable comparison.

If you need open weights, multimodal input or 256K context, Kimi K2.6 is one of the most important candidates in the public evidence here, but the lack of a complete same-source four-way comparison means it needs direct validation in your own stack.

The safest conclusion is simple: use public benchmarks to decide where to begin, then let your real tasks decide what goes into production.