GPT-6 Astra vs Muse Spark 1.3 vs Gemini 3.8 Flash
The Short Answer
Four frontier models shipped inside 48 hours in early September 2026. Google released Gemini 3.8 Flash and Meta released Muse Spark 1.3 on September 2; OpenAI released GPT-6 Astra and Anthropic released Claude Fable 5.1 on September 3.
Three of them aim at genuinely different jobs:
- GPT-6 Astra ($10/$50) — hardest reasoning, and the most token-efficient model measured.
- Muse Spark 1.3 ($1.25/$4.25) — long-horizon agents and the best long-context retrieval published.
- Gemini 3.8 Flash ($0.75/$3.75) — cheapest list price, broadest modality support, introductory rate expiring.
Last verified: September 4, 2026.
Side by Side
| GPT-6 Astra | Muse Spark 1.3 | Gemini 3.8 Flash | |
|---|---|---|---|
| Lab | OpenAI | Meta | Google DeepMind |
| Released | Sep 3, 2026 | Sep 2, 2026 | Sep 2, 2026 |
| Input / MTok | $10.00 | $1.25 | $0.75 (intro) |
| Output / MTok | $50.00 | $4.25 | $3.75 (intro) |
| Cached input / MTok | $1.00 | ~$0.15 | not listed at launch |
| Price from Jan 1, 2027 | unchanged | unchanged | $1.50 / $7.50 |
| Context window | 1,050,000 | 1,000,000 | 1,000,000 |
| Max output | 128,000 | — | 64,000 |
| Input modalities | Text, image | Text | Text, image, audio, video |
| DeepSWE v1.1 | 74.1%* | 75.4% | ~71–74% |
| Terminal-Bench 2.1 | — | 88.8% | ~89–91% |
| MRCR 512K–1M | — | 98.1% | — |
| AA Coding Agent Index | 67.0 (xhigh) | 64.2 (xhigh) | 61.1 (high) |
| Cost per coding task | $3.27 | $1.72 | $2.04 |
| Knowledge cutoff | Apr 30, 2026 | — | Mar 2026 |
* GPT-6 Astra’s DeepSWE figure comes from the leaked launch table and is not independently verified.
The Price Ladder Is a Lie (Sort Of)
Read the price row and Gemini 3.8 Flash looks 13x cheaper than GPT-6 Astra on input. Read the cost-per-task row and the ordering inverts.
| Agent configuration | Coding Agent Index | Cost per task |
|---|---|---|
| Codex · GPT-5.6 Luna (max) | 57.1 | $0.29 |
| Opencode · Gemini 3.7 Flash (high) | 59.6 | $1.27 |
| Codex · GPT-6 Astra (low) | 62.6 | $1.41 |
| Muse Code · Muse Spark 1.3 (xhigh) | 64.2 | $1.72 |
| Opencode · Gemini 3.8 Flash (high) | 61.1 | $2.04 |
| Codex · GPT-6 Astra (xhigh) | 67.0 | $3.27 |
| Claude Code · Fable 5.1 (max) | 70.4 | $9.18 |
Muse Spark 1.3 is the value pick on this table — higher index than Gemini 3.8 Flash at lower cost per task. And GPT-6 Astra (low) beats Gemini 3.8 Flash (high) on both axes despite the 13x price gap.
The mechanism is token consumption. Astra emits roughly 2,200–14,000 output tokens per task; Gemini 3.8 Flash needs about 48,000. Meta measured Muse Spark 1.3 using roughly 20% fewer tool calls and 25% fewer tokens than Muse Spark 1.2. Verbose models re-read more context and churn more calls, so a low per-token price does not survive contact with a real agent loop.
This does not make cheap tiers pointless — GPT-5.6 Luna at $0.29 per task is still the cheapest thing on the board. It means cost per completed task is the only number worth optimising, and you cannot read it off a pricing page.
Where Each One Actually Wins
GPT-6 Astra — hardest reasoning. Its ARC-AGI-3 result of 62.7% on ARC Prize’s official standard harness more than doubles Claude Opus 5’s 30.2% and dwarfs GPT-5.6 Sol’s 7.8%. Every Astra effort level sits on the Artificial Analysis Pareto frontier for tokens per point of intelligence. The catch: staged rollout through enterprise Trusted Access first, no fine-tuning and no realtime audio at launch, and prompts above 272,000 tokens reprice at a long-context premium.
Muse Spark 1.3 — long-horizon agents and long context. Meta reports 98.5% on MRCR 256K–512K and 98.1% on MRCR 512K–1M, against 91.5%/73.8% for GPT-5.6 Sol and 66.3%/55.5% for Muse Spark 1.2. That is not incremental tuning; it is a different retrieval capability. It also posts 75.4% on DeepSWE v1.1, ahead of Claude Opus 5 (74.0) and GPT-5.6 Sol (73.0), plus 66.9% partial on OSWorld 2.0 and 1754 on GDPval-AA v2. Meta trained it to sustain longer missions: juggling multiple workflows in one thread, asking clarifying questions on ambiguous prompts, confirming before consequential actions, and resisting prompt injection. Two gaps: a max reasoning mode is still finishing safety testing, and the promised open-weights release has not landed.
Gemini 3.8 Flash — modality breadth and volume floor. It is the only one of the three accepting text, image, audio and video input, with customisable effort levels and immediate GA. For high-volume production agents where cost per task decides viability, it remains a default. The catch is the January 1, 2027 price doubling to $1.50/$7.50 — a pilot that pencils out at $4,000/month today becomes $8,000/month with no usage change.
The January Cliff Nobody Budgets For
Gemini 3.8 Flash’s $0.75/$3.75 is an introductory rate that expires December 31, 2026. GPT-6 Astra and Muse Spark 1.3 carry no announced expiry.
If you are sizing a 2027 budget, model Flash at the post-intro price from day one. Two defensive moves: use Batch and Flex tiers at half rate for anything that does not need synchronous latency, and keep the model identifier behind a config flag so a December tier swap is a deploy rather than a refactor.
How to Choose
Default to a router, not a model. Classify each request: bounded, high-volume work to a Flash tier; open-ended reasoning and debugging to a frontier model. Teams that do this consistently land the majority of volume on the cheap tier while quality-sensitive work still reaches the top.
If you must pick one: Muse Spark 1.3 is the best all-round value on current numbers — frontier-class agentic coding, the best published long-context retrieval, and the lowest cost per coding task of the three. Pick GPT-6 Astra when the work is genuinely hard reasoning and you can wait for access. Pick Gemini 3.8 Flash when you need audio or video input, or when raw volume economics dominate and you have budgeted for January.
Verify before you commit. Meta published no independent third-party table at launch, Astra’s leaked benchmarks remain unverified, and vendor tables are directional rather than reproducible. Run a hundred representative tasks of your own and compare total spend against completion rate.