Where the models stood in Q1
The tier-one picture entering Q1 2026 was OpenAI o1 at the top for reasoning tasks, with GPT-4o as the versatile workhorse. Claude 3.5 Sonnet remained the writing and analysis preference for many professionals. Gemini 2.0 had launched but not yet found its distinctive positioning.
What changed in Q1 was the emergence of Gemini 2.5 Pro as a genuine tier-one option — specifically for tasks involving very long documents, large codebases, and multimodal inputs. The 1 million token context window crossed from marketing claim to practical capability as the model's quality within long contexts improved enough to be reliable.
Below the top tier, the open-weight story became impossible to ignore. Llama 3.3 70B and DeepSeek V3 were performing at a level that would have been considered firmly mid-tier closed-model performance six months earlier. For organisations able to self-host, the economic case for open weights became compelling across a wider range of tasks.
In Q4 2025, reasoning models were specialist tools — you used them deliberately for hard problems. In Q1 2026, every major lab released or upgraded reasoning-capable models, and the interfaces started enabling extended thinking by default for complex queries.
The practical effect: the average quality of AI responses to complex questions improved significantly, without users necessarily changing their behaviour. The models started thinking more automatically.
Gemini 2.5 Pro's 1 million token context window had been available for months, but Q1 2026 was when production use cases started emerging at scale. Legal firms processing entire case histories. Financial analysts feeding complete earnings call transcripts and filing archives. Software teams providing entire repository context for architecture questions.
The shift was less about the capability existing and more about teams figuring out how to use it effectively. Prompting strategies for very long contexts are different from short-context prompting, and Q1 saw the first real body of practical guidance emerge.
The continued price reductions across major providers — driven partly by DeepSeek competitive pressure — pushed per-token costs to the point where cost was no longer a meaningful constraint for most business use cases. The conversation shifted from 'how do we manage AI costs' to 'how do we maximise AI value' — a fundamentally different framing.
What changed for non-technical professionals in Q1
If you use AI for research and analysis
Gemini 2.5 Pro became the default recommendation for any task involving very long documents. If you regularly work with lengthy reports, contracts, or datasets, Q1 was the quarter to switch your analysis workflow to a long-context model.
If you manage AI tool selection for a team
The open-weight conversation became impossible to ignore. If your organisation has any technical capacity at all, the Q1 2026 open-weight models deserve evaluation. The cost and privacy advantages are now substantial enough to justify the setup overhead for high-volume use cases.
If you're tracking the competitive landscape
Q1 was a stabilisation quarter — consolidating late 2025's dramatic shifts rather than introducing new ones. Claude 4 Opus was the most anticipated release of Q2, and the early signals coming from Anthropic suggested a meaningful quality jump was coming.
- State of LLMs — Q2 2026 10 min
- State of LLMs — Q4 2025 8 min
- The Context Window Arms Race 3 min
- Model Tracker — current rankings Reference