SQE Leaderboard/Step 3.7 Flash

Step 3.7 Flash

cyankiwi/Step-3.7-Flash-AWQ-INT4

fourlineCommunity AWQ-INT4 SWE-bench Pro 56.3% community2026-08-02
Methodology
Token budget. 3 of 5 reps aborted on token budget. Those reps still count toward the score.

SQE score

10

blended mean, 5 reps

Gate

0/5

all reps counted

Wilson 95% low

≥0%

lower confidence bound

Shots to green

2

median of 5 reps

Every published metric

SQE score
Headline blended mean percent across reps (gate 45 / pass 25 / repair 15 / style 10 / thrift 5).
10n=5
Gate
Reps that cleared known and held-out honesty checks, shown as k/n after tainted exclusions.
0/5n=5
Pass
Reps that cleared public verification (compile, known conformance, scenario threshold), as k/n. Distinct from gate, which also requires held-out honesty.
1/5n=5
Wilson 95%
Lower bound of the gate pass rate at 95% confidence.
≥0%n=5
Harness ±
Blend of all score components, mean ± spread.
9.6 ±38.5n=5
Shots to green
Feedback rounds needed before tests pass. Lower is better.
2n=5
Scenario pass
Share of graded scenarios passing in the green build.
0%n=5
Trap/spec rate
Share of trap parameter values correctly synthesized from the spec.
0%n=5
1-shot gap
How much the first shot trailed the final result.
0.0 ptsn=5
Effective n
Reps counted after tainted-run exclusion.
5/5n=5
Median tokens / rep
Prompt + completion tokens per rep, median over untainted reps. Lower is better.
4.01M tokn=5 untainted
Min VRAM
Catalog minimum cluster VRAM (GB) from syndicAI referenceGpuRequirements. Closed APIs and packs not in the catalog show as -.
162 GB
SWE-bench Pro
Catalog SWE-bench Pro score for the resolved syndicAI model, or the vendor-published score for closed-reference models. Inferred scores are labelled. Packs without a score show as -.
56.3%

Run configuration

Endpoint model
cyankiwi/Step-3.7-Flash-AWQ-INT4
SWE-bench Pro
56.3% · SWE-bench Pro · StepFun (Step-3.7-Flash)
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-4-5

Performance Review

Step-3.7-Flash-AWQ-INT4 on the fourline task: Failed comprehensively across 5 runs. Zero gate passes (0/5); only 1 of 5 runs achieved any scenario pass (20% overall). 4 of 5 hit token budget exhaustion; the remaining run plateaued without improvement. Repair attempts were ineffectual (median shotsToGreen: 2 in the single converging case; the rest single-shot or plateauing).

The model demonstrated marked difficulty recovering from tool-use errors and logical mistakes. All trap scenarios (board-cols, board-rows, start-player) yielded neither, indicating the final code failed to execute or validate behavior at all. Most critically, every run failed compilation — the model repeatedly generated syntactically or semantically malformed TypeScript, applied identical edits that had no effect, and ran out of tokens before achieving a correct implementation. Runs r1, r4, r5 produced uncompilable code from the start; r3 achieved 83.3% scenario pass but still failed the ≥90% gate.


Strengths

  • Proactive verification: Model consistently initiated pnpm typecheck and pnpm lint self-checks (all 5 reps).
  • Tool read discipline: Properly read SPEC and GUIDELINES upfront in most runs.
  • Low tool-call errors relative to turns: Runs r2, r3, r4, r5 had 2–5 tool errors across 98–109 turns (relatively low frequency).

Failure Modes

  • Widespread compilation failure: 4 of 5 final shots did not compile. Errors include incorrect .ts imports, readonly array type mismatches, unused imports/vars, missing allowImportingTsExtensions in tsconfig, and property not found on Window.
  • Ineffectual edits: Multiple runs logged edit_file ERROR: No changes to apply (r1 turns 53, 58, 63, 75; r5 turns 62, 70), suggesting the model made identical old_string and new_string replacements—indicating confusion or failure to track state.
  • Stalling (reasoning-only loops): r1 turn 72 and r5 turns 81, 82 entered "no tools" reasoning-only modes, nudged by harness, wasting tokens on internal deliberation.
  • finish rejections: r5 called finish prematurely at turn 7, claiming code was done when it was manifestly incomplete.
  • Token burn without convergence: All 5 runs exhausted the token budget (2.3–3.9M prompt tokens). No run converged to a green pass; r2's single-shot success (40.14 harnessScore) was an outlier, likely overfitting to known-OK tests but failing held-out conformance.

Serving & Harness Notes

  • Context exhaustion: Turns ranged 98–360 per run; most ran 100+ turns hitting token limits. The longest run (r4, 360 turns) achieved only 3.02% harnessScore and spent 82% of token budget with minimal code progress.
  • Tool-call error patterns: Sparse but sticky (edit_file no-ops, old_string not found). Not symptomatic of high-bit quantization noise; rather, model state tracking failure.
  • Single-shot + plateauing repair curves (r1, r3, r4, r5): Model generated code, ran into errors, made minimal or zero progress on retries. Did not converge.
  • Early termination risk: r5 attempted finish at turn 7 with compiling code—model misjudged readiness.

Root Cause

This is not a quantization artifact. AWQ INT4 can sustain competitive coding; the issue is task execution discipline and state tracking under complexity. The model:

  • Failed to maintain correctness across sequential edits (copy-paste placeholders, identical edits).
  • Did not adequately debug compilation errors (e.g., missing tsconfig options).
  • Wasted significant tokens on redundant reasoning or tool loops without recovery strategy.
  • Could not scale the multi-file interaction (types, logic, main UI) reliably within a single shot.

Recommendation for ops: This model is not production-ready for medium-complexity TypeScript tasks requiring multi-file coordination and iterative debugging. Simpler single-file tasks may fare better. Consider fallback to a larger or higher-precision variant for this workload.

Per-rep results