All models

AlibabaChinaΒ· released Jul 2026

Qwen3.7 Flash

Provisional: 3 comparable tests so far. Ranked once it reaches 6 across 3 counting categories.

Alibaba, China. One of 19 models from this lab on the Index.

Third-party publishedLow confidence (thin coverage)

Snapshot September 8, 2026

OpenCharts Index

67.4

Provisional: not enough comparable tests or counting categories to rank yet Β· 3 comparable tests Β· 0 counting categories

Drop any single test and the Index lands between 50.8 and 83.9.

β€”HARD SET

Needs more results (1 of 8 so far)

Averaging 20.6 on the 1 it has; the score opens at 4 of 8.

Distance to today's frontier on the hardest tests. Not a measure of intelligence.

What it's known for

Qwen3.7 Flash, in 3 results.

Its widest gap is on FrontierMath (Tiers 1–3), 79.4% behind the best published result. No comparable results yet in knowledge, coding, agentic, multimodal, human preference and long context. Between 30% and 35% behind the best published results, on average.

Strongest category

No category counts yet: a category counts once it has more than one comparable test and the model has taken at least half of them. The scores it has are below.

Best single result

Mock AIME 2024–2025

Solve competition mathematics problems that have a single exact integer answer.

86.7%

86.7% of the best (100.0%) Β· #49 of 78.

Olympiad-style problems need long, careful chains of reasoning, and there is no partial credit.

Where it trails

FrontierMath (Tiers 1–3)

Solve unpublished research-level mathematics problems written by professional mathematicians (tiers 1 to 3).

19.3%

79.4% behind the best published result (93.7%) Β· #58 of 61.

Problems that take a working mathematician hours. A score here is a score against the frontier of the field.

Not measured yet

No comparable result yet in these 6 categories. A blank is a blank, never a zero, and it does not lower the Index.

  • Knowledge
  • Coding
  • Agentic
  • Multimodal
  • Human preference
  • Long context

Covered, but not averaged into the Index:

  • Reasoning1 of 6 tests Β· needs 3 Β· mean 81.0
  • Math2 of 5 tests Β· needs 3 Β· mean 53.7
Every score, with its source

See it at work

What Qwen3.7 Flash was asked to do, and how it did.

Every test it has taken, by category, with the kind of task it faced, who produced the number and where the result landed against the best published one. The examples are original and illustrative, never items from the datasets themselves.

Reasoning Β· mean 81.0 across 1 comparable test Β· not counted toward the Index yet (fewer than half the category's tests)

GPQA DiamondEpoch-run

Answer a PhD-level multiple-choice question in physics, chemistry or biology that a web search will not settle.

82.3%

81.0% of the best (95.8%), above chance
#64 of 78 Β· n = 198

Frontier models cluster in the eighties and nineties; Epoch's standard errors on this test are 2 to 3 points.

Source: Epoch AI (internal runs)

Every result is read against the best published one on its test: percent scores as a share of the best above chance, Arena ratings as the expected win rate against the board leader, open-ended values on a log scale. Sitting a harder exam never lowers a model. Every number links to the publisher that produced it.

Head to head

Qwen3.7 Flash against whoever you pick.

Choose a rival. Every counted test both have taken appears side by side as a share of the best published result on that test, with the category means above. A win is a higher share; a gap under 2 points is a tie, because one item on a 45-problem exam is 2.2 points and publishers' own standard errors are of that order.

Qwen3.7 Flash

provisionalΒ·Index 67.4C+

0 : 3

wins Β· 0 ties Β· 3 shared

GPT-6 Astra

#1Β·Index 99.0A+

GPT-6 Astra comes out ahead on 3 of the 3 tests both have taken.

Category means

  • Reasoning

    81.0
    99.6
  • Knowledge

    β€”
    100.0
  • Coding

    β€”
    97.7
  • Math

    53.7
    99.8
  • Agentic

    β€”
    98.5

Shared tests Β· biggest gaps first

Bars are distances to the best published result on each test (percent scores above chance, Arena ratings as win rate against the leader, open-ended values on a log scale). Raw values as the publishers report them.

Open GPT-6 Astra

Scores

Every test, every source

The best published run per benchmark, the raw value as the publisher reports it, who produced the number, how close it comes to the best published result on that test, and where the model sits among every model scored on it. Percent scores are read above chance, Arena ratings as a win rate against the board leader, open-ended values on a log scale. A test with too few models to compare against is shown but not counted.

Category means

  • Reasoning81.0 Β· 1/6
  • Knowledgeβ€”
  • Codingβ€”
  • Math53.7 Β· 2/5
  • Agenticβ€”
  • Multimodalβ€”
  • Human preferenceβ€”
  • Long contextβ€”

Mean Β· tests taken of the category's comparable tests. A faded category either rests on fewer than half of them or is a single-test category (marked β€œbeside”), which is shown next to the Index and never averaged into it.

GPQA DiamondEpoch-runReasoning

82.3%

81.0% of the best (95.8%), above chance
16th percentile of 78 Β· n = 198

Source: Epoch AI β€” AI Benchmarking Hub

86.7%

86.7% of the best (100.0%)
32th percentile of 78 Β· n = 45

Source: Epoch AI β€” AI Benchmarking Hub

19.3%

20.6% of the best (93.7%)
5th percentile of 61 Β· n = 290

none

Source: Epoch AI β€” AI Benchmarking Hub

Top of the leaderboard

Compare with its neighbours.

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