SQE Leaderboard/MiniMax M2.5
MiniMax M2.5
QuantTrio/MiniMax-M2.5-AWQ
SQE score
57
blended mean, 5 reps
Gate
3/5
all reps counted
Wilson 95% low
≥23.1%
lower confidence bound
Shots to green
1
median of 5 reps
Every published metric
Run configuration
- Endpoint model
- QuantTrio/MiniMax-M2.5-AWQ
- SWE-bench Pro
- 55.4% · SWE-bench Pro · MiniMax (M2.5)
- Reps
- 5
- Max shots
- 3
- Feedback tier
- 1
- Context cap
- 200K
- Token budget / run
- 4M
- Turn cap / shot
- 120
- Thinking budget
- 4.096K
- Sandbox digest
- quality-eval-sandbox@sha256:242ebd2f73c1c1ffa90c3ccb7913574f91e74a89dd1e4cf874f907a3129149c5
Review
Generated by claude-haiku-4-5Overall Verdict
The model achieves 60% gate pass rate (3/5 runs) on the fourline Connect-Four task, with strong performance when successful but concerning inconsistency. Three runs reach full correctness with single-shot repair and 93–95% overall scores; two runs plateau without recovery, hitting logic bugs (gravity/win-detection failures) that don't self-correct despite additional turns. Passing runs compile cleanly, pass all 14 scenarios including held-out conformance, and exit efficiently. Failing runs incur 110–120 turns and 3.7–4M prompt tokens without convergence, suggesting the model struggles to debug certain state-machine semantics once initial implementation diverges from correct behavior.
The 4-bit quantization (AWQ) is not a credible root cause: passing runs demonstrate precise TypeScript and game-logic reasoning under identical constraint. Failures instead reflect agent-level issues—difficulty diagnosing why gravity-drop returns null instead of "Y", or why win-state transitions remain stuck in "playing"—suggesting the model needs stronger introspection or error-message parsing to break cycles.
Strengths
- Reliable when correct: 3/5 runs compile, pass all 14 scenarios, and clear held-out conformance on first attempt.
- Self-directed quality: All runs self-initiate typecheck and lint; passes respect functional linting constraints (const, readonly).
- Token thrift in success cases: Passing runs use 0.40–0.71 token-thrift score despite high prompt volume (1.7–3.5M tokens), indicating reasonable code generation efficiency.
- No overfitting: Trap specs and held-out scenarios unambiguously passed in green runs.
Failure Modes
- Logic bugs don't self-repair: r2 and r5 fail on
gravity-drop(returnsnullinstead of piece color) and/orwin-vertical/conformance-replay(state remains"playing"instead of transitioning to"won"). Despite 3–6 tool-call errors per run and extended deliberation (110 turns in r2), the model does not recover; repair curve plateaus. - Tool-call error clustering: Failing runs incur 5–6 tool errors vs. 2–4 in passing runs, suggesting repeated format misalignment or harness friction during repair loops.
- Context burn without convergence: r2 consumes 3.76M prompt tokens and 693 wall-clock seconds across 3 shots without reaching green; token thrift drops to 0.37, indicating long non-productive turns.
Serving & Harness Notes
- Turn counts: Passing runs settle in 66–74 turns; failing runs expand to 110–120 turns without escape condition, suggesting the harness should enforce stricter repair-attempt limits or require explicit strategy restart.
- No reasoning-only stalls observed: All runs complete with status
completed; no hung inference. - Compile always succeeds: Type-safety preserved even in failed logic runs, reducing risk of runtime crashes in deployment.