CADLE CAP
91
Cadle #31/385 · 77% confidence
Model portfolio
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Alibaba · Released 2026-08-26 · v2026-08-26 · Open
Qwen3.8-Flash-Next is an open-source model from Alibaba (released 2026-08-26). Context window size is not listed with capabilities: Text, Code, Reasoning. List pricing ≈ $0.15 / $0.47 per 1M input/output tokens. Top tracked signals: MATH · benchlm 95.7, MATH 95.7, GPQA · benchlm 92.3. Cadle merges provider listings, unified leaderboard medians, and public pricing where available.
Language Cap
91
Cadle Language #31/385 · 77%
Benchmark average
84.4
#38 on the benchmark matrix
Pricing
$0.15
$0.47 / 1M out · 140.1 pts/$
CROSS-BOARD RANKS
CADLE CAP vs MODEL MATRIX
Cadle Cap ranks models on every published score we have for them — harder benchmarks weigh more. Missing tests are skipped, not zeroed. It answers who is stronger than whom in today's pool.
Model Matrix is the datasheet: Overall, Intelligence, Coding, Agentic, MMLU, GPQA, Math, LiveCodeBench, HumanEval, Reason, ARC-AGI. A dash means unmeasured. AVG is an importance-weighted mean of that model's filled cells only.
MODEL MATRIX · TRUTH CENTER CELLS
Overall
62.8
AA IQ
39.8
Coding
77.0
Agentic
85.3
MMLU
79.8
GPQA
92.3
Math
95.7
LiveCodeBench
71.3
HumanEval
—
Reason
91.2
ARC-AGI
—
Coverage AVG
84.4
Cadle Cap ranks models with within-benchmark percentiles so ARC-AGI ~40 and MMLU ~90 never compete raw. Coverage AVG is an importance-weighted mean of filled expert cells only — blanks are skipped, not zeroed.
ANALYSIS · TRUTH CENTER
Hard benches (GPQA · ARC-AGI · LCB · Agentic) sit left — a mid-40 on ARC-AGI can still be frontier. Broader benches on the right are easier and often higher.
Only measured signals
CADLE CAP
91
Cadle #31/385 · 77% confidence
INTELLIGENCE
48.0
Media Cap quality axis
CODING
77.0
Media Cap quality axis
AGENTIC
85.3
Media Cap quality axis
MATH
95.7
Media Cap quality axis
MATRIX OVERALL
62.8
AVG 84.4 across matrix cells
BENCHMARK AVG
78.0
22 tracked scores
STRONGEST SIGNAL
MATH · benchlm
Score 95.7
Measured matrix / AA / gateway rows only.
Qwen3.8-Flash-Next is an open-source model from Alibaba (released 2026-08-26). Context window size is not listed with capabilities: Text, Code, Reasoning. List pricing ≈ $0.15 / $0.47 per 1M input/output tokens. Top tracked signals: MATH · benchlm 95.7, MATH 95.7, GPQA · benchlm 92.3. Cadle merges provider listings, unified leaderboard medians, and public pricing where available.
PROVIDER
Alibaba
RELEASE
2026-08-26
STATUS
ACTIVE
OPERATOR NOTES
Quality, latency, cost, and failure modes — not marketing copy.
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First review sets the tone for how teams evaluate Qwen3.8-Flash-Next.
| Metric | |
|---|---|
| Provider | Alibaba |
| Release | 2026-08-26 |
| Input / 1M | $0.15 |
| Output / 1M | $0.47 |
| Context | — |
| Cadle Cap | 91 |
| Cap board | Cadle Language #31/385 |
| MATH · benchlm | 95.7 |
| MATH | 95.7 |
| GPQA · benchlm | 92.3 |
| GPQA | 92.3 |
| GPQA · artificial-analysis | 92.3 |
| LIVECODEBENCH · benchlm | 91.9 |
| REASON · benchlm | 91.2 |
| REASON | 91.2 |
| AGENTIC · artificial-analysis | 86.1 |
| AGENTIC | 85.3 |
| AGENTIC · benchlm | 84.5 |
| CODING · benchlm | 81.0 |
Scores may use different scales — check the Benchmarks tab for source links.
Evidence linked to this model — listing, hub, benchmarks, or live gateway prices.
API
Alibaba listing
API
artificial-analysis
API
OpenRouter
COMMUNITY
Papers with Code
COMMUNITY
BenchLM
COMMUNITY
unified-median
Same Truth Center numbers as the datasheet above. Accuracy, reliability, safety, and cost scores are not invented.