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Qwen: Qwen3.6 Flash

Qwen: Qwen3.6 Flash is qwen's model. On the un-leaked meo benchmark it ranks #21 of 82 with 69.5% accuracy and an Effective Value (𝕍) of 0.0070 β€” earning a correct answer for about $0.0157 in 147s each.

as of 2026-10-18

meo rank
#21
of 82
Accuracy
69.5%
overall
𝕍 index (top=100)
0.17
raw 𝕍 0.0070 Β· N=10
$ / correct
$0.0157
147s each

Per-domain accuracy

173 correct of 250 questions asked across 11 domains Β· answer rate 100% Β· macro accuracy 76.9%. Headline accuracy is micro-averaged per meo’s methodology; per-domain question counts (correct/asked) are shown so you can judge significance.

Tape machine
100%15/15
Theory of mind
100%3/3
CSP (unsatisfiable)
94%15/16
Regex automaton
94%15/16
Base-rate bias
88%36/41
Logic / math / CS
76%26/34
Framework bias
67%2/3
Perceptual illusions
53%54/102
Watson-Glaser
50%2/4
Long arithmetic
33%5/15
State tracking
0%0/1

Efficiency

Cost per correct
$0.0157
Seconds per correct
147
Tokens per correct
13,896
Correct answers
173 / 250
Total benchmark cost
$2.72
Avg tokens / item
9,616

Model details

Released
2026-04-27
Weights
β€”
Context
1M
Max output
66K
Knowledge cutoff
β€”
Price in
$0.19/M
Price out
$1.13/M
Modality
text+image+video->text

Sources: openrouter Β· meo

FAQ

How accurate is Qwen: Qwen3.6 Flash?

Qwen: Qwen3.6 Flash scores 69.5% overall on the meo benchmark (rank #21 of 82), an un-leaked private holdout graded objective-first.

What is Qwen: Qwen3.6 Flash's Effective Value?

Its Effective Value (𝕍) is 0.0070 at N=10, Ο‰=1, Ξ΄=1.5 β€” a fused metric of accuracy, speed, cost, and error-cascade. 𝕍 rankings shift with task depth.

How much does Qwen: Qwen3.6 Flash cost per correct answer?

About $0.0157 per correct answer (13,896 tokens, 147 seconds each) at benchmark-time pricing via OpenRouter.

Is Qwen: Qwen3.6 Flash open weights?

Open-weights status for Qwen: Qwen3.6 Flash is not recorded.

Benchmark updates affecting this model

  • 2026-06-09majorlogic_math_cs ground truth was broken β€” culled and rebuilt with solver-verified items

    A 5-lab frontier jury revealed that 29 of 37 LLM-authored logic_math_cs items had broken ground truth: on most, five independent frontier models unanimously concluded the clue sets were contradictory while the stored answer claimed a number, and on eight, all five agreed on the SAME different number (author arithmetic errors). Models were being marked wrong for being right. The 29 were retired (full audit published in the repo) and the domain rebuilt with generator-as-oracle items: an embedded constraint solver PROVES each puzzle has exactly one solution (or provably none), verified by an independent exhaustive enumerator. A 3-round difficulty study found frontier models saturate consistent human-scale logic puzzles, so items use anonymous global constraints and carry a measured difficulty gradient.

  • 2026-06-08majorCorrected base_bias grading (LLM-equivalence judge fix)

    The LLM-equivalence grader uses a reasoning model whose verdict token budget was too small, so its YES/NO answer was emitted as empty and silently counted as a failure. This understated the base_bias domain to ~7% for every model when the true accuracy is ~85-90%. We raised the judge budget, excluded its reasoning tokens from the verdict, switched to verbosity-robust conclusion matching, and re-graded the whole board.

Full benchmark changelog β†’

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Related: all Qwen (Alibaba) models Β· Tape-machine simulation domain Β· best Effective Value Β· biggest movers Β· methodology Β· full leaderboard.