Use-case benchmarks

The best model is the one that ships your work.

We don't sell you the cheapest token or chase a single contested leaderboard number. srooter benchmarks by developer use case and routes each request to the model that maximises productivity — automatically, behind one endpoint. Below is how we route, and the evidence behind it.

Implement or refactor a feature across many files

Coding agent

srooter keeps real multi-file coding on the model you asked for (Claude), and offers MiniMax-M2 as a near-frontier, far-cheaper alternative when you opt in. We never silently downgrade a coding turn.

srooter routes to

Claude Opus

Anthropic

Productivity outcome

First-try PR correctness — fewer review round-trips

Evidence · SWE-bench Verified

Claude Opus 4.8 ~88.6% · MiniMax-M2 ~80% (open-weight leader)

swebench.com

Use-case × model fit

Use caseClaude Opus4.8MiniMax-M2M2MiniMax-M3M3Gemini3.1 ProDeepSeekV3.2Qwen332B
Coding agentrouted
Whole-repo / long contextrouted
Architecture & security reviewrouted
Quick edits & housekeepingrouted
Bulk extraction & classificationrouted
Hard debugging & reasoningrouted
Best fitStrongOKsrooter routes here

Models reference

The models srooter routes across, with versions and where each shines. SWE-bench Verified figures are external evidence (approximate — see note).

Claude Opus4.8

Anthropic

Frontier coding, architecture & security judgment

Context:
200K (1M beta)
SWE-bench:
~88.6%
MiniMax-M2M2

MiniMax

Near-frontier coding at a fraction of the cost

Context:
~205K
SWE-bench:
~80% (open-weight leader)
MiniMax-M3M3

MiniMax

Whole-repo / long-context work without truncation

Context:
1M
SWE-bench:
Gemini3.1 Pro

Google

Multimodal, bulk extraction, grounded research

Context:
1M+
SWE-bench:
~80.6%
DeepSeekV3.2

DeepSeek

Strong reasoning & debugging, very low cost

Context:
128K
SWE-bench:
~73%
Qwen332B

Alibaba

Instant trivial/housekeeping turns (served via Groq)

Context:
256K
SWE-bench:

External benchmarks are shown as evidence, sourced and dated (verified June 2026); treat them as approximate. SWE-bench Verified is known to be partly contaminated (OpenAI moved to SWE-bench Pro in early 2026) — which is exactly why we lead with use-case fit, not one number. The fit ratings are srooter's routing recommendations, not measured scores.