Which of Qwen3.8-Max-0902, Muse Spark 1.3 and GPT-6 Astra is cheapest?

Muse Spark 1.3 is cheapest on list price at $1.25 per million input and $4.25 output, followed by Qwen3.8-Max-0902 at $2 / $6 and GPT-6 Astra at $10 / $50 — an 8x spread on input between the ends. On measured cost per completed coding task the gap narrows sharply: Muse Spark 1.3 at extra-high effort lands near $1.72 per task and GPT-6 Astra at low effort near $1.41, because Astra emits far fewer output tokens per task.

Is Qwen3.8-Max-0902 a new model or an update?

An update. Released September 2, 2026, it is a post-training snapshot of Qwen3.8-Max with the same 2.4-trillion-parameter architecture, the same 1M-token context window and the same $2 / $6 pricing. Alibaba post-trained it on what the Qwen team calls Coding and Cowork, so the gains show up on agentic coding and office-task benchmarks rather than on raw reasoning.

Which model should I use for agentic coding in September 2026?

Muse Spark 1.3 for cost-sensitive agent fleets — it uses roughly 20% fewer tool calls and 25% fewer tokens than Muse Spark 1.2 and scores about 62 on the Artificial Analysis Intelligence Index at a fifth of Astra's token price. GPT-6 Astra for the hardest long-horizon work, where its 67.0 index at extra-high effort justifies $10 / $50. Qwen3.8-Max-0902 if you want a 1M-context Chinese-hosted model with a coding-tuned snapshot at a middle price.

Does Qwen3.8-Max-0902 beat Claude Opus 5?

No, not on the aggregate indices. Qwen3.8-Max sits near 57.7 on the Artificial Analysis Intelligence Index against Claude Opus 5 at 63.0 and Claude Fable 5.1 at 65.6. The 0902 snapshot improves coding and agentic scores over the earlier Qwen3.8-Max checkpoint without closing that gap. Its argument is price and context, not frontier capability.

Quick Answer

Qwen3.8-Max-0902 vs Muse Spark 1.3 vs GPT-6 Astra

Published: September 6, 2026

The Short Answer

Three models landed inside 36 hours at the start of September 2026, and they occupy three different price tiers:

	Qwen3.8-Max-0902	Muse Spark 1.3	GPT-6 Astra
Vendor	Alibaba	Meta	OpenAI
Released	Sep 2, 2026	Sep 2, 2026	Sep 3, 2026
Input / MTok	$2	$1.25	$10
Output / MTok	$6	$4.25	$50
Cached input	$0.17 explicit	~$0.15	$1.00
Context	1,000,000	1,000,000	1,050,000
Max output	131,072	—	128,000
AA Intelligence Index	~57.7	~62.0	61.1 (max)
Input modalities	text	text only	text + image

The cheapest token is not the cheapest task. On the Artificial Analysis coding-agent harness, GPT-6 Astra at low effort completes a task for about $1.41 at index 62.6, while Muse Spark 1.3 at extra-high effort costs about $1.72 at index 64.2. An 8x token-price gap collapses to roughly parity once you count how many tokens each model actually burns.

Qwen3.8-Max-0902 — Same Model, Coding-Tuned

$2 / $6 per MTok · 2.4T parameters · 1M context · released September 2, 2026

This is not a new architecture. Alibaba shipped a post-training snapshot of Qwen3.8-Max: same 2.4-trillion-parameter base, same 1M-token context, same price sheet. The Qwen team describes the post-training target as “Coding & Cowork” — agentic software work and office-style multi-step tasks — and the announcement went out on X rather than through a formal model card.

The 0902 suffix follows Alibaba’s dated-snapshot convention, so qwen3.8-max-0902 and the base qwen3.8-max are separately addressable. If you pin model strings in production, pin the snapshot; if you point at the alias, you inherited this change on September 2 whether you planned for it or not.

Pricing detail worth knowing: cache hits are quoted at $0.17 explicit and $0.25 implicit on QwenCloud — the first time the Max tier had been priced on the API Alibaba introduced with Qwen3.8-Flash. Explicit caching being cheaper than implicit is unusual and rewards workloads that manage their own cache keys.

Strengths: 1M context at a mid-tier price, a genuinely large max output at 131,072 tokens, and a coding-focused delta over the previous checkpoint.

⚠️ Limits: the aggregate index still sits around 57.7 — behind Muse Spark 1.3, Gemini 3.8 Flash (58.7), GLM 5.3 (59.4) and every Western frontier model. Chinese API endpoints may be excluded by your data-residency policy. And because the announcement was informal, several trackers carry slightly different benchmark tables — cite the one you actually measured against.

Muse Spark 1.3 — Best Index Per Dollar

$1.25 / $4.25 per MTok · 1M context · text-only input · released September 2, 2026

Meta’s post-Llama line, and the value pick of this group. The headline change in 1.3 is behavioural rather than raw capability: it is trained to act like a careful teammate — asking clarifying questions when a request is vague, requesting help when stuck, and confirming before irreversible steps.

That restraint has a measurable payoff. Meta reports roughly 20% fewer tool calls and 25% fewer tokens than Muse Spark 1.2 on the same work. In an agent loop, fewer tool calls means fewer round trips, less latency and a smaller bill — the compounding kind of efficiency that never shows on a price sheet.

At roughly 62.0 on the Artificial Analysis Intelligence Index it edges past GPT-6 Astra’s 61.1 at max effort while costing an eighth as much per input token.

Strengths: best index-per-dollar here, 1M context, cached input near $0.15, and an agent-safety posture that reduces destructive mistakes.

⚠️ Limits: text-only input — no image, audio or video, which rules it out of any multimodal pipeline. Its max reasoning mode was still in safety testing at launch. Open weights are on the roadmap but have not shipped, so do not plan self-hosting around it. And its prices come from trackers rather than a prominent official Meta pricing page — confirm against your first invoice.

GPT-6 Astra — Most Capable, Most Expensive

$10 / $50 per MTok · 1,050,000 context · 128K max output · released September 3, 2026

OpenAI’s flagship, and the only model here with image input, a frontier-grade long-horizon agent story, and a genuine effort dial: low → medium → high → xhigh → max.

That dial is the whole cost story. Per-task output tokens run 2,200 to 14,000 depending on effort, against roughly 48,000 for Gemini 3.8 Flash. Measured coding-task cost climbs from $1.41 at low effort (index 62.6) through $3.27 at xhigh (67.0) to $4.72 at max. Astra at low effort is competitive with models a tenth its token price; Astra at max is a deliberate, expensive choice for work that justifies it.

⚠️ Two hard warnings. First, prompts above 272,000 tokens reprice — input roughly doubles to about $20/MTok, and the output multiplier has been reported inconsistently between 1.5x and 2x, so verify against billing before designing a long-context workload. Second, access is staged: enterprise Trusted Access first, then API, ChatGPT plans and AWS. Availability on the day you read this may not match the announcement.

Strengths: the highest ceiling of the three, image input, the largest context, and a low-effort tier that is cheaper per task than its price sheet suggests.

Limits: the 272K repricing cliff, no fine-tuning and no realtime audio at launch, and an index of 61.1 that — despite the AGI framing around the launch — does not lead the Artificial Analysis Intelligence Index. Claude Fable 5.1 (65.6) and Claude Opus 5 (63.0) sit above it.

Cost on a Real Workload

A 30,000-token input / 5,000-token output task, at list rates:

Model	Cost per task
Muse Spark 1.3	$0.059
Qwen3.8-Max-0902	$0.090
GPT-6 Astra	$0.550

Astra is roughly 9x Muse Spark 1.3 on this shape. But this arithmetic assumes equal token consumption, which is exactly the assumption that breaks in agent loops. Run the measured per-task numbers instead when the workload is agentic.

How to Choose

If you need…	Use
Cheapest capable agent fleet	Muse Spark 1.3
Fewest tool calls per task	Muse Spark 1.3
1M context on a mid budget	Qwen3.8-Max-0902
Very long single outputs (>128K)	Qwen3.8-Max-0902
Image input	GPT-6 Astra
Hardest long-horizon agent work	GPT-6 Astra (xhigh/max)
Best raw index regardless of vendor	Neither — Claude Fable 5.1 at 65.6

The last row matters. All three of these launched in the same news cycle, which makes them feel like the frontier. On the aggregate index they are not: Anthropic’s Fable 5.1 sits above all three, and Claude Opus 5 above two of them.

Last verified: September 6, 2026.