The cleanest deflation datapoint is Grok 4.6. SpaceXAI reports an Artificial Analysis Intelligence Index score of 61, up from 56 for Grok 4.5. Its API price starts at the same $2 per million input tokens and $6 per million output tokens charged for 4.5.
That is not a token-price cut. It is capability deflation: more intelligence offered at the old meter rate. If the improvement raises your first-pass success rate or removes retries, cost per successful task can fall even when the invoice line items do not.
Grok 4.6: the vertical move
SpaceXAI released Grok 4.6 on August 12 with a focus on long-running agents and interactive work. The company reports 61 on the AA composite, 69.9% on CursorBench 3.2, and 65.9% on DeepSWE 1.1. It also reports a longer supplemental training run and refreshed post-training trajectories. Those figures are useful because 4.5 is shown in the same launch table, but they remain benchmark evidence—not a guarantee for a specific workflow. (SpaceXAI announcement)
The API keeps a 500k-token context window and charges $2/$6, with cached input at $0.50 per million tokens. Requests above 200k context can use different rates, so the headline price is not the bill for a repository, research loop, or multi-hour agent run. (Grok 4.6 API documentation)
Qwen3.8-Max: the horizontal pressure
Qwen3.8-Max is a different signal. QwenCloud describes it as a 2.4-trillion-parameter mixture-of-experts model, and lists a second endpoint named qwen3.8-2.4t-a95b. It accepts text, images, and video; supports a 1M-token context window; and allows up to 131k output tokens. (Qwen3.8-Max model page)
Its standard API price is also $2/$6. The sharper datapoint is repeated context: implicit cached input is $0.25 per million tokens and explicit cache reads are $0.17. For workflows that resend a stable codebase, document set, policy library, or tool schema, the cache design can matter more than the uncached sticker price.
Qwen positions 3.8-Max as its strongest reasoning model and maps it to the highest-capability tier, but its current product documentation does not cite an independently measured task-cost result. That distinction is important: the architecture and prices are facts; the task economics still need to be measured. (QwenCloud model guide)
| Datapoint | Grok 4.6 | Qwen3.8-Max |
|---|---|---|
| Release | Aug 12, 2026 | Current flagship |
| API list price | $2 input / $6 output | $2 input / $6 output |
| Cached input | $0.50 | $0.25 implicit / $0.17 read |
| Context | 500k | 1M |
| Measured AA score | 61 | Not cited in current product docs |
| Architecture disclosure | Not disclosed | 2.4T MoE; A95B endpoint |
The metric that belongs on your curve
Cost per million tokens is an input. For an operating decision, the more useful unit is cost per successful task: model calls, retries, tool fees, latency, and human review divided by outcomes that clear a fixed quality bar.
Grok 4.6 may lower that number by completing more work per attempt. Qwen3.8-Max may lower it through model competition, cache economics, or a better fit for multimodal and long-context tasks. The only defensible way to know is to run both against the same task set, rubric, harness, and reviewer policy—then repeat the assessment as the frontier moves.