ONGOING
Tracks regressions over time, not one-off rankings
It keeps running comparable cases, so it is useful for seeing whether a model gets lazier or regresses in how it reads context, not just where one score lands.
[ BENCHMARK // LIVE ARENA ]
Results live in Arena
[ BENCHMARK // WHAT IT MEASURES ]
What it measures, what it does not
SCOPE
Not a direct test of capability level
It does not directly judge whether coding output is correct, whether a math answer is right, or whether generated text matches a product requirement.
TOOLS
Biased toward tool-calling behavior
It mostly watches read, write, search, and similar tool calls. A low score here usually means the model is a poor fit for agent work.
LAZY
Checks for lazy early writes
If a model starts writing before it has read enough context, it gets penalized. Frontier models also get nerfed at times, and one goal here is to notice that shift early.
CASE
Case-driven and customizable
The standard comes from each case's prompt, rootfs, and scoring rule. You can run LLMSnare yourself and change cases for different scenarios and standards.
LIMITS
These dimensions are not scored directly
Latency, cost, throughput, long-context limits, multi-tool orchestration stability, and side-effect control in real repositories are not part of the score today.
EXPLAIN
Every bonus and deduction is explainable
Each case ties bonuses and deductions to explicit behaviors. You can trace the rules to see why a score moved.
[ CASES // FAILURE PATTERNS ]
Failure behaviors it identifies
Common behavior failures an agent makes in real tasks
PATTERN 01
Acts before it understands the situation
For example, a task requires writing under a style guide, but the model starts writing without reading the style guide file first. That shows weak instruction following.
PATTERN 02
Knows it is on the wrong path, but keeps going
For example, an instruction may contain a small mistake, but tool calls are enough to recover the correct context. If the model still stays on the old wrong path after seeing the correct information, its recovery is weak.
PATTERN 03
Gets stuck in ambiguity
For example, the model notices ambiguity, but the surrounding context is enough to reach the correct conclusion. If it keeps making low-value explorations and still gets it wrong, that points to behavioral problems.
[ UPDATES // RECENT CHANGES ]
Update log
Recent changes that affect how this benchmark should be read.
2026-04-19 23:00 UTC
Changed the update cadence to every 3 hours and removed Claude Sonnet 4.5 and Claude Opus 4.5.
2026-04-17 02:00 UTC
Added Claude Opus 4.7, removed OpenAI GPT 4.1 and Claude Haiku 4.5, added the new
search_text tool, and raised the difficulty.2026-04-12
Added two models: Google’s Gemma 4 31B and Xiaomi’s Mimo v2 Pro.
2026-04-11
Raised the difficulty because too many models were hitting full score.
2026-04-10
Launched the live LLMSnare benchmark.