Today we're introducing tAI 4.1, at the time the largest and most capable model in the tAI family. It is built for professional, knowledge-heavy work: reasoning through ambiguous instructions, writing and reviewing real code, and carrying a long, multi-step task through to the end without losing the plot.
tAI 4.1 is available today across the Artfical ecosystem: through the tAI API, the developer console, and at tai.artfical.com.
Model performance
With tAI 4.1 we focused on depth: a model that can sit with a genuinely hard, open-ended problem for a long time without losing coherence, rather than one tuned mainly to sound confident on the first attempt.
Economically valuable tasks
We evaluated tAI 4.1 against a broad set of well-specified, knowledge-based tasks spanning dozens of professional fields, including spreadsheet modeling, presentation drafting, structured writing, research synthesis, and technical documentation. Across these tasks, tAI 4.1 shows a clear, consistent improvement over the tAI 4.0 series, particularly on tasks that require carrying constraints across several steps rather than answering in a single pass.
The gain isn't concentrated in any one field. It's broadest on tasks that require carrying several constraints at once and getting the whole thing right in one pass, a spreadsheet model with formulas that stay consistent when a number changes, a summary that doesn't quietly drop a caveat from the source, a draft that respects every stated requirement instead of most of them.
Coding
tAI 4.1 was trained heavily on the Artfical Code Index V11.6, our internally compiled corpus of code, commit history, and tool-use trajectories. In practice this shows up as fewer broken edits, better handling of large existing codebases, and steadier behavior across long agentic coding sessions where the model has to plan, write, run, read the output, and revise, repeatedly, without supervision.
Beyond raw code generation, it's better at working inside an existing project's own conventions, matching the style already in a file rather than imposing its own, and at debugging: given a stack trace or a failing test, it's more likely to find the actual cause before proposing a fix rather than pattern-matching to a plausible-looking one.
Agentic tool use
A growing share of real usage is no longer a single question and answer. Increasingly it's a model operating tools on someone's behalf over an extended session: querying a database, editing files, calling an API, checking the result, and deciding what to do next. tAI 4.1 was trained specifically to be more reliable in this loop, with meaningfully fewer dropped steps and less drift from the original goal as sessions get longer.
A big part of that reliability is knowing when not to act, pausing to ask before an ambiguous instruction turns into the wrong file being edited or the wrong record being changed, rather than picking the most likely interpretation and continuing.
Long context
Long, messy, real-world context, a full repository, a stack of documents, a sprawling conversation history, is where models most often lose track of what actually matters. tAI 4.1 is noticeably steadier here: it holds onto earlier constraints and instructions further into a session, and is less likely to quietly contradict something it was told at the start.
We tested this directly by planting a constraint early in a long, otherwise unrelated session and checking whether the model still honored it dozens of turns later, without being reminded. tAI 4.1 does this measurably more often than the tAI 4.0 series, which is a large part of why long working sessions with it feel less like starting over each time.
Multilingual performance
tAI is used heavily in Turkish, not just English, so multilingual quality has never been a secondary concern for us the way it can be for models tuned mostly against English benchmarks. tAI 4.1 carried forward the Turkish-weighted portion of the ArtficalAI corpus with a deliberate emphasis on natural, non-translated-sounding responses, idioms, and informal register, on top of the reasoning and coding gains that carry over regardless of language.
"tAI 4.1 was the first version where I stopped re-explaining myself halfway through a long session." (internal testing note, Artfical DT)
Training data
tAI 4.1 was trained end to end on data we collected and compiled ourselves. No third-party corpus was used as a base. Two datasets anchor the model:
- ArtficalAI: a multilingual, Turkish-weighted general text corpus covering reasoning, conversation, and general knowledge.
- Artfical Code Index V11.6: a compiled corpus of code and repository data built specifically for agentic coding and tool-use.
Both datasets are versioned and re-mixed between training runs rather than frozen after being built once, weighted toward the kinds of tasks our own usage data shows people actually run tAI on.
Benchmarks
Scores from other providers are the official results those providers have published; tAI 4.1 scores were measured by Artfical.
Availability
tAI 4.1 has been available since May 5, 2026, at tai.artfical.com, through api.artfical.com for developers, and through the developer console for key management and usage tracking. Input costs $0.90 per million tokens and output costs $2.00 per million tokens.
It remains selectable in the model picker alongside every model that shipped after it, including tAI 4.2, so a chat or project pinned to tAI 4.1 keeps working exactly as it did on release day.
We keep sharing what we learn as more people put tAI 4.1 to work on real, long-running tasks. That feedback is what shapes every version after it.