Multi-Environment Web Challenge (MEWC)
We show this table for reference; we do not rank on it.
A benchmark that evaluates AI agents on multi-environment web challenges, testing navigation and task completion across diverse live web environments.
Benchmark score on MEWC — October 17, 2026
We compile the MEWC rows from secondary reports. MiniMax M2.5 leads the table at 74.4%. We do not use these results to rank models overall.
1 modelAgenticCurrentDisplay onlyUpdated October 17, 2026
Benchmark score table (1 model)
| Rank | Model / configuration | Score | Parameters (B) | Open / closed |
|---|---|---|---|---|
| 1 | MiniMax M2.5MiniMax | 74.4% | Not reported | Closed |
About MEWC
Year
2026
Tasks
Web-agent tasks
Format
Browser task completion
Difficulty
Open-web agent workflows
MEWC is useful as an agentic browsing benchmark because it focuses on open-web interaction and multi-environment task execution rather than single-site scripted browsing.
Freshness and provenance
Version
MEWC 2026
Refresh cadence
Quarterly
Staleness state
Current
Question availability
Public benchmark set
BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.
Questions
What does MEWC measure?
A benchmark that evaluates AI agents on multi-environment web challenges, testing navigation and task completion across diverse live web environments.
Which model scores highest on MEWC?
MiniMax M2.5 by MiniMax currently leads with a score of 74.4% on MEWC.
How many models are evaluated on MEWC?
1 AI models have published results on MEWC in the BenchLM catalog.
Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
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