TIMETOACT LLM Benchmarks June 2026

The main conclusion is clear: the top model is still on top, but the gap has become much smaller.

GPT o1 pro (manual) remains the overall leader with a final score of 97. However, the next group is now extremely close: Qwen3.7 Max, GPT-5.5 and GPT-5.5 Pro all reach a final score of 95. This is an important shift. The market no longer looks like a race with one isolated leader. Several models are now operating at a level where the choice depends less on raw benchmark position and more on cost, latency, deployment model, privacy requirements and integration strategy.

The most striking result in this benchmark is Qwen3.7 Max reaching second place.

With a final score of 95, Qwen3.7 Max performs at the same level as the strongest OpenAI models directly below the leader. It reaches top or near-top scores in several key enterprise categories, including Code+Eng, CRM, Docs, Integrate and Reason.

This is a strong signal for the market. Until recently, many non-frontier or locally oriented models were discussed mainly as "good enough" alternatives for selected use cases. Qwen3.7 Max changes that perception. It shows that models outside the usual Western frontier-model narrative can compete at the very top of enterprise benchmarks.

For companies, this changes the question. It is no longer enough to ask: "Which model is the strongest?" The better question is now: "Which model provides the right quality, at the right cost, with the right deployment and compliance profile for this specific workload?"

At the same time, OpenAI remains exceptionally strong. OpenAI models occupy many of the leading positions in the benchmark, including the overall top model and several models in the top tier.

This matters because enterprise adoption is rarely about a single isolated task. Companies need models that perform consistently across coding, document processing, structured business data, API integration, reasoning and communication tasks. In this benchmark, OpenAI's portfolio remains very strong across that full spectrum.

However, the results also show that a more expensive model is not automatically the best business choice. GPT-5.5 and GPT-5.5 Pro both achieve a final score of 95, but their estimated costs differ significantly. GPT-5.4 Pro reaches a very strong final score of 94 and has the highest Reason score in the table, but it is also one of the more expensive options. Meanwhile, ChatGPT Chat Latest reaches 93 and looks like a strong balanced model for scenarios where quality, speed and practical usability all matter.

This is exactly why benchmarking only raw quality is not enough. In real projects, model selection must include quality, price, speed and operational constraints.

One of the most interesting findings is that high-quality models are now available at very different price points.

Several models close to the top of the benchmark are far less expensive than the premium frontier options. Google Gemini 3.1 Pro Preview reaches a final score of 90 with an estimated cost of €0.54. GPT-4o v3/2024-11-20 reaches 89 at €0.63. GPT-5.4 reaches 89 at €0.74. DeepSeek V4 Flash is especially notable, with a final score of 88 and an estimated cost of only €0.09.

That does not mean the cheapest model is always the best. But it does mean that companies can now design more efficient AI architectures. Instead of relying on one universal model for everything, they can use a portfolio of models:

• a frontier model for difficult, high-risk or high-value tasks;
• a strong but cheaper model for high-volume workloads;
• a local or locally deployable model for privacy-sensitive or infrastructure-sensitive scenarios;
• and smaller specialized models for routing, extraction, classification or preprocessing.

Another positive trend is the improvement of models that can be run locally or closer to a company's own infrastructure.

The benchmark includes several non-cloud or locally oriented models with final scores above 80, including Qwen3.6 27B, Gemma 4 31B IT, Qwen 2.5 72B Instruct, GLM 5.1, Gemma 4 26B A4B IT and Nous Llama 3.1 405B Hermes 3.

This is important for organizations with strict requirements around data sovereignty, security, latency, infrastructure control or predictable cost. Local models are no longer just an experimental option. For selected enterprise workflows, they are becoming a realistic part of the architecture.

The most promising use cases are not necessarily full replacement of frontier models. Local models can already be highly valuable for classification, extraction, internal assistants, document preprocessing, workflow automation, retrieval pipelines and low-latency backend tasks.

In several categories, the upper part of the table is already very crowded. Multiple models achieve scores of 100 in Code+Eng, CRM, Docs or Integrate. This suggests that many enterprise capabilities are becoming broadly available across providers.

Reasoning remains more difficult.

The highest Reason score in this benchmark is 90, achieved by GPT-5.4 Pro. Many otherwise strong models perform very well in coding, document processing or integration tasks, but score noticeably lower in reasoning. This distinction is important. A model can be good at producing code or extracting structured information while still struggling with multi-step logic, edge cases, business rules or complex decision-making inside a constrained context.

For enterprise adoption, this is a key lesson: generic public leaderboards are useful, but they are not enough. Companies need to test models on workloads that look like their own processes: internal documents, product data, APIs, CRM systems, compliance rules, SAP, Salesforce, ServiceNow, knowledge bases and agentic workflows.

The main practical takeaway is that LLM selection has become an architectural decision.

In 2026, choosing a model is no longer just about selecting the highest score on a leaderboard. A serious enterprise AI architecture must consider:

• model quality on the specific workload;
• cost at realistic token volumes;
• speed and latency;
• availability of local or private deployment;
• integration with the existing cloud stack;
• reasoning quality;
• reliability of structured outputs;
• data protection and compliance requirements;
• and the ability to route tasks between several models

For some workloads, the best choice will still be a top OpenAI model. For others, Gemini, Claude, Qwen or DeepSeek may be more attractive. For privacy-sensitive or cost-sensitive workflows, a local model may be the better architectural fit.

Increasingly, the best answer is not one model. The best answer is a model strategy.

This benchmark shows how quickly the LLM market has matured. The overall leader is still strong, but the distance to the next models has become much smaller. OpenAI continues to dominate the top positions, Qwen3.7 Max delivers the most impressive breakthrough, Google and Claude remain strong enterprise contenders, DeepSeek shows excellent cost efficiency, and local models are becoming increasingly practical.

For businesses, this is good news. Competition is increasing. Quality is improving. Costs are becoming more flexible. Deployment options are expanding.

The next phase of enterprise AI will not be defined only by who uses the newest or most powerful model. It will be defined by who can benchmark, select, combine and integrate the right models into real business processes.