Qwen3.8 Max Matches Claude Opus 4.8 on Intelligence Index, But Costs 2x More Per Task Than Predecessor

Alibaba's Qwen3.8 Max scores 56 on the Artificial Analysis Intelligence Index, according to Artificial Analysis, a 10-point jump over its predecessor Qwen3.7 Max (46). That score puts it on par with Anthropic's Claude Opus 4.8 and ahead of Zhipu AI's GLM-5.2 (51), but it still trails Moonshot AI's Kimi K3, which scores 57 — one point higher — while running 25 percent cheaper per task.

Benchmark gains come with a cost

On GDPval-AA, a benchmark for work-related tasks measured in Elo points, Qwen3.8 Max jumped 468 points to 1,739, moving past Kimi K3 (1,685). Only Claude Opus 5 scores higher, at 1,852.

The gains come with a tradeoff in efficiency. Qwen3.8 Max required 64 steps per task on GDPval-AA, compared to 14 for the previous version. Input token consumption grew 15x, largely because the benchmark resends the full conversation history at each step — a design detail that penalizes models needing more steps to complete a task.

Alibaba cut per-token pricing for Qwen3.8 Max: input tokens dropped from $2.50 to $2.00 per million, output tokens from $7.50 to $6.00, and cached input tokens from $0.50 to $0.25 per million. Despite the lower rates, the increased step count and token volume pushed the effective cost per task on the Intelligence Index to $1.14 — more than double the $0.53 per task for Qwen3.7 Max.

By comparison, Kimi K3 costs $0.86 per task despite scoring one point higher, and GLM-5.2 costs just $0.57 per task. Alibaba's price-to-performance ratio worsened even as list pricing dropped, because the model's higher token consumption outweighed the per-token savings.

Accuracy didn't improve — confidence did

Artificial Analysis also recorded regressions on two benchmarks. AA-LCR, which tests a model's ability to correctly synthesize information from long documents, dropped 2 points compared to Qwen3.7 Max. AA-Omniscience, which measures whether a model answers correctly or honestly declines when it doesn't know, fell 10 points.

The accuracy rate on AA-Omniscience held steady at around 31 percent, but the hallucination rate rose from 23 percent to 40 percent. Qwen3.8 Max answers more often instead of admitting uncertainty, according to Artificial Analysis, even though its actual accuracy didn't improve.

What this means

Qwen3.8 Max illustrates a pattern showing up across frontier model updates: raw intelligence scores can rise while real-world cost efficiency falls. Alibaba's model now matches Claude Opus 4.8 on a composite benchmark, but the path to that score — 64 steps and 15x more input tokens per task — reveals a model that compensates for reasoning gaps with brute-force iteration rather than sharper judgment.

The rising hallucination rate is the more concerning data point. A model that guesses instead of admitting uncertainty 40 percent of the time, up from 23 percent, is a regression in reliability even if headline scores improve. For teams choosing between Qwen3.8 Max, Kimi K3, and GLM-5.2, the Artificial Analysis data suggests Kimi K3 remains the more cost-efficient choice for now, and buyers evaluating any new model release should weigh per-task cost and hallucination rate alongside composite intelligence scores.