Key takeaways
- Three serious launches landed in 48 hours. Meta released Muse Ember on 9 November, Anthropic released Claude Fable 5.2 and Mythos 5.2 the same day, and Google made Gemini 4 Carbon generally callable on 10 November.
- The price spread is 80x on input tokens. Ember is reported at $0.10 / $0.50 per million input/output tokens, Carbon at $2 / $10 (introductory), and Fable 5.2 at $8 / $40.
- Carbon's launch price is temporary. Google's introductory rate reportedly doubles to $4 / $20 from 1 December. Any cost estimate you make this month should use the higher number.
- Fable 5.2 is cheaper than the model it replaces. Anthropic cut the price by about 20% versus Fable 5.1, as VentureBeat reported.
- Our advice: do not pick one model. Route high-volume, simple steps to a cheap tier, and keep the expensive tier for planning, hard code and review. For most Indian products that cuts the monthly bill by more than half without a visible quality drop.
1. What shipped, in one paragraph each
Meta Muse Ember (9 Nov). Meta's launch post positions Ember as the smallest and most efficient model in the Muse family, built for repetitive, high-volume agent work. Coverage from MarkTechPost and Digital Trends reports a 1M-token context window and pricing of $0.10 per million input tokens. Meta's own benchmark numbers put it well ahead of other budget models on agent-automation tasks. Treat those as vendor claims until independent tests appear.
Claude Fable 5.2 and Mythos 5.2 (9 Nov). Anthropic's announcement introduces both as its top models for long, open-ended technical work. Fable 5.2 is priced at $8 / $40 per million tokens, about 20% below Fable 5.1. MarkTechPost describes the pair as the same underlying model with different safeguards, and Mythos 5.2 has its own docs page. Anthropic also reports that Fable 5.2 at high effort matches its previous top model's best score at roughly half the cost per task.
Google Gemini 4 Carbon (10 Nov). Google's post makes Carbon available to paid API users and AI Ultra subscribers, with a developer guide in AI Studio. The introductory price is $2 / $10, and several outlets, including FourWeekMBA, report that it rises to $4 / $20 on 1 December. Google says Carbon tops several agentic and coding benchmarks, including CursorBench.
2. Side-by-side
| Muse Ember | Gemini 4 Carbon | Claude Fable 5.2 | |
|---|---|---|---|
| Vendor | Meta | Anthropic | |
| Released | 9 Nov 2026 | 10 Nov 2026 | 9 Nov 2026 |
| Input / output price (per 1M tokens) | $0.10 / $0.50 (reported) | $2 / $10 intro, $4 / $20 from 1 Dec (reported) | $8 / $40 |
| Context window | 1M (reported) | See developer guide | 1M (per Opper's listing) |
| Built for | High-volume agent steps, extraction, classification | Agentic coding, tool use, multimodal work | Long, hard technical tasks; planning; review |
| Best use in a stack | The "worker" tier | The default "smart" tier | The "escalation" tier |
| Main risk | Young model, benchmarks mostly vendor-reported | Price doubles in three weeks | Cost if used for every call |
Benchmarks move every week and each vendor picks the ones it wins. The prices are the firm part of this table, so plan around them.
3. What it costs in rupees
Take a typical mid-sized Indian SaaS feature: a support or operations agent that reads 10 million input tokens and writes 2 million output tokens a month. We use ₹90 to the dollar for round numbers. Add 18% GST on the imported service.
| Model | Monthly cost (USD) | Approx. ₹ (before GST) |
|---|---|---|
| Muse Ember | $2 | ₹180 |
| Gemini 4 Carbon (intro, till 30 Nov) | $40 | ₹3,600 |
| Gemini 4 Carbon (from 1 Dec) | $80 | ₹7,200 |
| Claude Fable 5.2 | $160 | ₹14,400 |
Now the routed version. Send 85% of the tokens (lookups, field extraction, ticket tagging, simple replies) to Ember. Send 15% (the hard cases, multi-step reasoning and final checks) to Fable 5.2:
- Ember share: 8.5M in + 1.7M out = $1.70
- Fable share: 1.5M in + 0.3M out = $24
- Total ≈ $25.70, or about ₹2,300 a month. That is roughly 84% less than running everything on Fable, and about a third of the cost of all-Carbon at the December price.
The exact split depends on your traffic, but the pattern holds. Most calls in a real product are easy, so the cheap tier sets your bill.
4. Which one should you pick?
Startups and student projects on a tight budget. Start with Ember for anything repetitive. Add Carbon at the intro price only for the steps where Ember fails your tests. Re-run your cost sheet before 1 December.
Product teams with an existing Gemini or Vertex AI setup. Carbon is the easy upgrade. Test it now while it is cheap, but budget at $4 / $20. If your GCP account bills through an Indian reseller or GCP's India entity, check how the new SKU appears on your invoice before you scale.
Teams doing heavy code generation, migrations or security review. Fable 5.2 is the one to evaluate. At high effort it is reported to match the previous flagship at about half the cost per task, so if you already pay for Anthropic's top tier, 5.2 should lower your bill. Do not put it in front of every user request.
Regulated workloads (BFSI, health, government). None of these launches changes the basics. Check data-residency options for an Indian region, the vendor's data-retention terms, and your obligations under the Digital Personal Data Protection Act. Keep personal data out of prompts where you can, whatever the model.
5. A one-week evaluation plan
| Day | Task |
|---|---|
| 1 | Export 200 real (anonymised) requests from your product logs as a test set, tagged easy / medium / hard |
| 2 | Run the full set on all three models through one gateway or router; log tokens, latency and cost per request |
| 3 | Score answers with a fixed rubric (correct, partially correct, wrong); have a human spot-check 30 |
| 4 | Find the cheapest model that passes each tag; that is your routing table |
| 5 | Add a fallback: if the cheap model's answer fails a check (schema, confidence, test), retry on the next tier |
| 6–7 | Ship to 10% of traffic; compare cost and complaint rate against the old setup |
Two practical points. First, measure cost per completed task, not cost per token, because a model that needs three retries is not cheap. Second, keep prompts and tool definitions model-neutral so switching vendors is a config change, not a rewrite.
6. What this means for Indian engineers' careers
The skill in demand is moving from "knows how to prompt one model" to "can run several models in production and keep the bill under control". That means evaluation sets, routing, caching, fallbacks and per-feature cost dashboards. If you are a fresher or switching into AI roles, build one small project that routes between a cheap and an expensive model and reports cost per task. It shows more than a certificate does.
Planning an AI feature or an agent and want the model choice and running costs worked out before you build? See our AI and automation development service, or read our guide on how to choose an LLM for your product.