Benchmark · agentic

τ²-bench

19 results 15 models

τ²-bench is an updated version of τ-bench that tests tool-using conversational AI agents on "dual-control" tasks, where both the agent and a simulated user can take actions to get a job done. The headline metric is the percentage of tasks solved (the average reward across tasks).

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Example
A tech-support scenario: the agent looks up a customer's account and follows the domain's policy while guiding a simulated user to carry out steps on their own device (for example, changing a setting), and the two must coordinate to resolve the issue.
Scoring
Each task ends with an automatic check of whether the final system/database state matches the required outcome, giving a reward per task; the reported score is the percentage of tasks solved (or the average reward).
Verification
Acceptance is fully programmatic — the environment compares the end state (and required actions) against the expected result with no human grading, so a task counts only when the outcome matches.
Why it matters
It matters because real assistants often can't act alone — they must instruct and coordinate with a user — and this dual-control setup exposes communication and coordination failures that single-actor tool benchmarks miss.
Worked example
Task
τ²-bench, telecom (dual-control) domain: a simulated user writes, 'Since this morning I have no mobile data, but calls and texts still work.' Given the carrier's policy document and tools — plus device actions the user can perform on their own phone — authenticate the customer, diagnose the fault, and restore mobile data.
Solution
Correct agent trajectory (telecom tools + user-side device actions): 1. get_customer_by_phone(number); confirm identity via name + DOB. 2. get_line_status(line_id) → "active"; get_network_status(region) → "operational" (rules out account suspension and network outage). 3. Follow the "data down / voice up" branch: user confirms Airplane Mode is OFF, then agent instructs the user to switch "Mobile Data" ON. 4. Verify data is restored; apply NO billing credit (policy forbids a fee/credit for a self-service fix). Resolution → user enables Mobile Data; connectivity restored, account state unchanged.
Walkthrough
With voice and SMS working while data is down on an active line with no network outage, the fault is device-side, so the policy troubleshooting tree resolves it by guiding the user to re-enable Mobile Data (and forbids any goodwill credit for a self-service fix). τ²-bench scores a per-task reward (0/1) by checking the environment/database reaches the expected end state and that all required actions and communicated info are present, then reports reliability across repeated trials via the pass^k metric.
0 25 50 75 100 2024-10-22 2025-09-14 2026-08-08 Claude 3.5 Sonnet · 69.2 · 2024-10-22 Claude 3.7 Sonnet · 81.2 · 2025-02-24 Kimi K2 · 66.1 · 2025-07-28 Kimi K2 · 66.1 · 2025-07-28 GLM-4.5 · 90.6 · 2025-08-06 GPT‑5 · 96.7 · 2025-08-07 GPT-5 · 63.5 · 2025-08-07 Kimi K2 Thinking · 93 · 2025-11-07 Kimi K2 Thinking · 93 · 2025-11-19 GPT-5.2 Thinking · 98.7 · 2025-12-11 GPT-5.2 Thinking · 98.7 · 2025-12-11 GPT-5.2 Thinking · 98.7 · 2025-12-11 Claude Opus 4.6 · 91.9 · 2026-02-05 Step 3.5 Flash · 88.2 · 2026-02-10 GPT-5.5 · 98 · 2026-04-25 ToolGraph+DPO · 35.5 · 2026-06-23 ToolGraph · 33.8 · 2026-06-23 AgentOmnia (starting from Qwen3-30B-A3B-Thinking-2507) · 41.7 · 2026-07-28 Pokee-Isaac 28B · 66.2 · 2026-08-08
Claude 3.5 Sonnet Claude 3.7 Sonnet Kimi K2 GLM-4.5 GPT‑5 GPT-5 Kimi K2 Thinking GPT-5.2 Thinking Claude Opus 4.6 Step 3.5 Flash GPT-5.5 ToolGraph+DPO ToolGraph AgentOmnia (starting from Qwen3-30B-A3B-Thinking-2507) Pokee-Isaac 28B
Timeline
Date Model Score Source
2026-08-08 Pokee-Isaac 28B 66.2% Pokee AI releases Pokee-Isaac 28B, a 10M-token context model for in-boundary deployment
2026-07-28 AgentOmnia (starting from Qwen3-30B-A3B-Thinking-2507) 41.69% AgentOmnia scales agentic models across full-scenario applications
2026-06-23 ToolGraph+DPO 35.5% Self-Evolution of Tool-Calling Agents via Divergence-Point Preference Learning
2026-06-23 ToolGraph 33.8% Self-Evolution of Tool-Calling Agents via Divergence-Point Preference Learning
2026-04-25 GPT-5.5 98.0% OpenAI releases GPT-5.5, a ground-up retrained model with native omnimodal processing
2026-02-10 Step 3.5 Flash 88.2% Step 3.5 Flash: open 11B-active MoE model achieves frontier-level agentic performance
2026-02-05 Claude Opus 4.6 91.9% Anthropic releases Claude Opus 4.6 with 1M context window and agentic improvements
2025-12-11 GPT-5.2 Thinking 98.7% OpenAI releases GPT-5.2, outperforming Gemini 3 in coding and reasoning benchmarks
2025-12-11 GPT-5.2 Thinking 98.7% OpenAI introduces GPT-5.2 with improved coding, long-context, and reasoning capabilities
2025-12-11 GPT-5.2 Thinking 98.7% OpenAI introduces GPT-5.2 with state-of-the-art professional knowledge work capabilities
2025-11-19 Kimi K2 Thinking 93.0% Moonshot AI releases Kimi K2 Thinking with agentic tool use
2025-11-07 Kimi K2 Thinking 93.0% Moonshot AI releases Kimi K2 Thinking, an open-source 1T parameter reasoning model
2025-08-07 GPT‑5 96.7% OpenAI releases GPT-5 API with coding and agentic improvements
2025-08-07 GPT-5 63.5% OpenAI launches unified GPT-5 with real-time routing and free access
2025-08-06 GLM-4.5 90.6% Zhipu AI releases GLM-4.5 models matching Claude and DeepSeek
2025-07-28 Kimi K2 66.1% Kimi K2: open MoE model with 1T parameters and MuonClip optimizer
2025-07-28 Kimi K2 66.1% Moonshot releases Kimi K2, an open agentic MoE model with 32B activated parameters
2025-02-24 Claude 3.7 Sonnet 81.2% Anthropic releases Claude 3.7 Sonnet with dynamic reasoning control
2024-10-22 Claude 3.5 Sonnet 69.2% Anthropic upgrades Claude 3.5 Sonnet, releases Claude 3.5 Haiku and computer use beta