Benchmark and pricing snapshot

DeepSeek’s naming is not perfectly consistent across public summaries. Pricing sources refer to DeepSeek V4 or DeepSeek V4 Pro, while some benchmark tables use DeepSeek-V4-Pro-Max. The table below keeps the source labels rather than treating every configuration as the same model.

1. Overall intelligence: GPT-5.5 leads the visible index, but not by enough to settle everything

Artificial Analysis lists the top visible Intelligence Index entries as GPT-5.5 xhigh at 60, GPT-5.5 high at 59, Claude Opus 4.7 Adaptive Reasoning, Max Effort at 57, followed by Gemini 3.1 Pro Preview and GPT-5.4 xhigh, also at 57.

That supports a narrow conclusion: among the visible entries in that summary, GPT-5.5 ranks ahead of Claude Opus 4.7. It does not support a full four-model leaderboard, because the same visible summary does not provide same-basis Intelligence Index scores for DeepSeek V4 or Kimi K2.6.

2. Agents and tool use: GPT-5.5 is the strongest visible pick

For agentic browsing, GPT-5.5 has the edge in the cited comparison, but DeepSeek is close. VentureBeat lists BrowseComp at 84.4% for GPT-5.5, 83.4% for DeepSeek-V4-Pro-Max and 79.3% for Claude Opus 4.7.

The gap is wider on Terminal-Bench 2.0, which Yahoo / Investing.com describes as testing command-line workflows. GPT-5.5 is listed at 82.7%, compared with 69.4% for Claude Opus 4.7 and 67.9% for DeepSeek in VentureBeat’s table.

Kimi K2.6 has a visible Terminal-Bench 2.0 score of 66.70%, but that number comes from a different comparison involving Kimi K2.6, Claude Opus 4.6 and GPT-5.4, not a same-room comparison with GPT-5.5, Claude Opus 4.7 and DeepSeek V4.

3. Reasoning and review: Claude Opus 4.7 has the clearest advantage

On the reasoning-heavy benchmarks shown in the VentureBeat summary, Claude Opus 4.7 leads. It scores 94.2% on GPQA Diamond, ahead of GPT-5.5 at 93.6% and DeepSeek-V4-Pro-Max at 90.1%. On Humanity’s Last Exam no-tools, Claude Opus 4.7 scores 46.9%, compared with GPT-5.5 at 41.4%, GPT-5.5 Pro at 43.1% and DeepSeek-V4-Pro-Max at 37.7%.

LLM Stats reaches a similar pattern for GPT-5.5 versus Claude Opus 4.7: across the 10 benchmarks both providers report, Claude Opus 4.7 leads six and GPT-5.5 leads four. The split is category-based, with Claude ahead on reasoning-heavy and review-grade tests, while GPT-5.5 is ahead on long-running tool-use tests.

4. Coding: Claude leads SWE-Bench Pro, but workflow matters

For software engineering, the headline depends on what kind of coding work you mean. DataCamp’s DeepSeek V4 comparison lists SWE-Bench Pro at 64.3% for Claude Opus 4.7, 58.6% for GPT-5.5 and 55.4% for DeepSeek V4 Pro. Yahoo / Investing.com also reports GPT-5.5 at 58.6% on SWE-Bench Pro and describes that benchmark as evaluating GitHub issue resolution.

Kimi K2.6 deserves its own test if your team is exploring coding agents. Verdent lists Kimi K2.6 at 58.60% on SWE-Bench Pro, 80.20% on SWE-Bench Verified and 89.60% on LiveCodeBench v6. But the same summary says the Kimi K2.6 figures come from Moonshot AI’s official model card and that SWE-Bench Pro used a Moonshot in-house harness.

So the practical coding takeaway is not one-size-fits-all. Claude Opus 4.7 has the highest visible SWE-Bench Pro score in the DeepSeek / GPT-5.5 / Claude table. GPT-5.5 looks stronger for terminal and tool-heavy workflows. Kimi K2.6 is a credible candidate for an open coding-agent trial, but its published numbers should not be dropped into the same four-way leaderboard without qualification.

5. Pricing and context: DeepSeek V4 is the cost outlier

The strongest case for DeepSeek V4 is price. Mashable lists DeepSeek V4 at $1.74 per 1 million input tokens and $3.48 per 1 million output tokens, with a 1 million-token context window. The same summary lists GPT-5.5 at $5 per 1 million input tokens and $30 per 1 million output tokens, and Claude Opus 4.7 at $5 per 1 million input tokens and $25 per 1 million output tokens, both also with 1 million-token context windows.

DataCamp uses the same pricing basis for DeepSeek V4 Pro, GPT-5.5 and Claude Opus 4.7, and lists each at around a 1 million-token context window. On the numbers available here, DeepSeek V4 is materially cheaper than GPT-5.5 and Claude Opus 4.7.

That makes DeepSeek V4 a natural first candidate for high-volume API routing, especially where its quality is already close enough for the task. In the same set of public data, DeepSeek-V4-Pro-Max scores 83.4% on BrowseComp, just behind GPT-5.5 at 84.4%.

Kimi K2.6 does not have a same-basis API price in the provided sources. DocsBot describes it as an open-source native multimodal agentic model from Moonshot AI with 256K context, aimed at long-horizon coding, coding-driven design, autonomous execution and swarm-based orchestration.

A practical deployment strategy: route by task, then test on your own workload

For most product teams, the best answer is not to standardise on one model immediately. Build a small evaluation suite and route tasks by risk, cost and workflow.

1. Use GPT-5.5 as the premium agentic baseline. It has strong public numbers on BrowseComp and Terminal-Bench 2.0. OpenAI also lists GPT-5.5 at 84.9% on GDPval, 78.7% on OSWorld-Verified and 98.0% on Tau2-bench Telecom, all benchmarks tied to tool use or knowledge-work workflows.
2. Use Claude Opus 4.7 for reasoning, review and low-tolerance work. It leads the cited GPQA Diamond and Humanity’s Last Exam no-tools comparisons, and LLM Stats says its advantages cluster around reasoning-heavy and review-grade tests.
3. Use DeepSeek V4 to reduce high-volume API cost. Its public token prices are below GPT-5.5 and Claude Opus 4.7, while its BrowseComp score is close to GPT-5.5 in the cited table.
4. Put Kimi K2.6 in the open-source coding-agent test pool. The available data show relevant coding and agentic scores, but not a clean four-way same-harness comparison with GPT-5.5, Claude Opus 4.7 and DeepSeek V4.

Key limitations

• Not every model has a same-harness benchmark. GPT-5.5, Claude Opus 4.7 and DeepSeek-V4-Pro-Max appear together in some VentureBeat benchmark tables, while Kimi K2.6 mainly appears in a separate comparison against Claude Opus 4.6 and GPT-5.4.
• Model settings vary. Artificial Analysis separates GPT-5.5 xhigh and high, lists Claude Opus 4.7 as Adaptive Reasoning, Max Effort, and VentureBeat uses DeepSeek-V4-Pro-Max.
• Self-reported and third-party scores are not identical evidence. LLM Stats explicitly cautions that some GPT-5.5 and Claude Opus 4.7 scores are self-reported at each provider’s high reasoning tier and are comparable in shape, not methodology.
• Benchmarks are task proxies, not production guarantees. BrowseComp focuses on agentic web browsing, Terminal-Bench 2.0 on command-line workflows, and SWE-Bench Pro on GitHub issue resolution.

Bottom line

If you are shortlisting from public data, GPT-5.5 is the strongest first test for agentic tool use and visible overall ranking; Claude Opus 4.7 is the strongest first test for reasoning-heavy and review-grade work; DeepSeek V4 is the most compelling cost-performance candidate for high-volume APIs; and Kimi K2.6 belongs in open-source coding-agent experiments, but does not yet have enough same-harness evidence for a fair four-way ranking.

Before buying or deploying at scale, run the models against the same real tasks: same prompts, same tools, same context length and the same success criteria. Public benchmarks should decide your testing order. Your workload, error cost and token budget should decide the final route.

Benchmark and pricing snapshot

DeepSeek’s naming is not perfectly consistent across public summaries. Pricing sources refer to DeepSeek V4 or DeepSeek V4 Pro, while some benchmark tables use DeepSeek-V4-Pro-Max. The table below keeps the source labels rather than treating every configuration as the same model.

1. Overall intelligence: GPT-5.5 leads the visible index, but not by enough to settle everything

Artificial Analysis lists the top visible Intelligence Index entries as GPT-5.5 xhigh at 60, GPT-5.5 high at 59, Claude Opus 4.7 Adaptive Reasoning, Max Effort at 57, followed by Gemini 3.1 Pro Preview and GPT-5.4 xhigh, also at 57.

That supports a narrow conclusion: among the visible entries in that summary, GPT-5.5 ranks ahead of Claude Opus 4.7. It does not support a full four-model leaderboard, because the same visible summary does not provide same-basis Intelligence Index scores for DeepSeek V4 or Kimi K2.6.

2. Agents and tool use: GPT-5.5 is the strongest visible pick

For agentic browsing, GPT-5.5 has the edge in the cited comparison, but DeepSeek is close. VentureBeat lists BrowseComp at 84.4% for GPT-5.5, 83.4% for DeepSeek-V4-Pro-Max and 79.3% for Claude Opus 4.7.

The gap is wider on Terminal-Bench 2.0, which Yahoo / Investing.com describes as testing command-line workflows. GPT-5.5 is listed at 82.7%, compared with 69.4% for Claude Opus 4.7 and 67.9% for DeepSeek in VentureBeat’s table.

Kimi K2.6 has a visible Terminal-Bench 2.0 score of 66.70%, but that number comes from a different comparison involving Kimi K2.6, Claude Opus 4.6 and GPT-5.4, not a same-room comparison with GPT-5.5, Claude Opus 4.7 and DeepSeek V4.

3. Reasoning and review: Claude Opus 4.7 has the clearest advantage

On the reasoning-heavy benchmarks shown in the VentureBeat summary, Claude Opus 4.7 leads. It scores 94.2% on GPQA Diamond, ahead of GPT-5.5 at 93.6% and DeepSeek-V4-Pro-Max at 90.1%. On Humanity’s Last Exam no-tools, Claude Opus 4.7 scores 46.9%, compared with GPT-5.5 at 41.4%, GPT-5.5 Pro at 43.1% and DeepSeek-V4-Pro-Max at 37.7%.

LLM Stats reaches a similar pattern for GPT-5.5 versus Claude Opus 4.7: across the 10 benchmarks both providers report, Claude Opus 4.7 leads six and GPT-5.5 leads four. The split is category-based, with Claude ahead on reasoning-heavy and review-grade tests, while GPT-5.5 is ahead on long-running tool-use tests.

4. Coding: Claude leads SWE-Bench Pro, but workflow matters

For software engineering, the headline depends on what kind of coding work you mean. DataCamp’s DeepSeek V4 comparison lists SWE-Bench Pro at 64.3% for Claude Opus 4.7, 58.6% for GPT-5.5 and 55.4% for DeepSeek V4 Pro. Yahoo / Investing.com also reports GPT-5.5 at 58.6% on SWE-Bench Pro and describes that benchmark as evaluating GitHub issue resolution.

Kimi K2.6 deserves its own test if your team is exploring coding agents. Verdent lists Kimi K2.6 at 58.60% on SWE-Bench Pro, 80.20% on SWE-Bench Verified and 89.60% on LiveCodeBench v6. But the same summary says the Kimi K2.6 figures come from Moonshot AI’s official model card and that SWE-Bench Pro used a Moonshot in-house harness.

So the practical coding takeaway is not one-size-fits-all. Claude Opus 4.7 has the highest visible SWE-Bench Pro score in the DeepSeek / GPT-5.5 / Claude table. GPT-5.5 looks stronger for terminal and tool-heavy workflows. Kimi K2.6 is a credible candidate for an open coding-agent trial, but its published numbers should not be dropped into the same four-way leaderboard without qualification.

5. Pricing and context: DeepSeek V4 is the cost outlier

The strongest case for DeepSeek V4 is price. Mashable lists DeepSeek V4 at $1.74 per 1 million input tokens and $3.48 per 1 million output tokens, with a 1 million-token context window. The same summary lists GPT-5.5 at $5 per 1 million input tokens and $30 per 1 million output tokens, and Claude Opus 4.7 at $5 per 1 million input tokens and $25 per 1 million output tokens, both also with 1 million-token context windows.

DataCamp uses the same pricing basis for DeepSeek V4 Pro, GPT-5.5 and Claude Opus 4.7, and lists each at around a 1 million-token context window. On the numbers available here, DeepSeek V4 is materially cheaper than GPT-5.5 and Claude Opus 4.7.

That makes DeepSeek V4 a natural first candidate for high-volume API routing, especially where its quality is already close enough for the task. In the same set of public data, DeepSeek-V4-Pro-Max scores 83.4% on BrowseComp, just behind GPT-5.5 at 84.4%.

Kimi K2.6 does not have a same-basis API price in the provided sources. DocsBot describes it as an open-source native multimodal agentic model from Moonshot AI with 256K context, aimed at long-horizon coding, coding-driven design, autonomous execution and swarm-based orchestration.

A practical deployment strategy: route by task, then test on your own workload

For most product teams, the best answer is not to standardise on one model immediately. Build a small evaluation suite and route tasks by risk, cost and workflow.

1. Use GPT-5.5 as the premium agentic baseline. It has strong public numbers on BrowseComp and Terminal-Bench 2.0. OpenAI also lists GPT-5.5 at 84.9% on GDPval, 78.7% on OSWorld-Verified and 98.0% on Tau2-bench Telecom, all benchmarks tied to tool use or knowledge-work workflows.
2. Use Claude Opus 4.7 for reasoning, review and low-tolerance work. It leads the cited GPQA Diamond and Humanity’s Last Exam no-tools comparisons, and LLM Stats says its advantages cluster around reasoning-heavy and review-grade tests.
3. Use DeepSeek V4 to reduce high-volume API cost. Its public token prices are below GPT-5.5 and Claude Opus 4.7, while its BrowseComp score is close to GPT-5.5 in the cited table.
4. Put Kimi K2.6 in the open-source coding-agent test pool. The available data show relevant coding and agentic scores, but not a clean four-way same-harness comparison with GPT-5.5, Claude Opus 4.7 and DeepSeek V4.

Key limitations

• Not every model has a same-harness benchmark. GPT-5.5, Claude Opus 4.7 and DeepSeek-V4-Pro-Max appear together in some VentureBeat benchmark tables, while Kimi K2.6 mainly appears in a separate comparison against Claude Opus 4.6 and GPT-5.4.
• Model settings vary. Artificial Analysis separates GPT-5.5 xhigh and high, lists Claude Opus 4.7 as Adaptive Reasoning, Max Effort, and VentureBeat uses DeepSeek-V4-Pro-Max.
• Self-reported and third-party scores are not identical evidence. LLM Stats explicitly cautions that some GPT-5.5 and Claude Opus 4.7 scores are self-reported at each provider’s high reasoning tier and are comparable in shape, not methodology.
• Benchmarks are task proxies, not production guarantees. BrowseComp focuses on agentic web browsing, Terminal-Bench 2.0 on command-line workflows, and SWE-Bench Pro on GitHub issue resolution.

Bottom line

If you are shortlisting from public data, GPT-5.5 is the strongest first test for agentic tool use and visible overall ranking; Claude Opus 4.7 is the strongest first test for reasoning-heavy and review-grade work; DeepSeek V4 is the most compelling cost-performance candidate for high-volume APIs; and Kimi K2.6 belongs in open-source coding-agent experiments, but does not yet have enough same-harness evidence for a fair four-way ranking.

Before buying or deploying at scale, run the models against the same real tasks: same prompts, same tools, same context length and the same success criteria. Public benchmarks should decide your testing order. Your workload, error cost and token budget should decide the final route.