How accurate are these prices?

Prices are approximate as of April 2026 using each provider's public API pricing. For Gemma 4 31B we use Together AI / Fireworks hosted pricing as a baseline, which tends to be the cheapest third-party host. Check the provider's official pricing page before committing to a production budget — contracts, enterprise discounts, and prompt caching can all change the effective rate.

What is Muse Spark's Contemplating Mode?

Contemplating Mode is Muse Spark's internal multi-agent reasoning feature released by Meta on April 8, 2026. When enabled, Muse Spark runs several parallel reasoning agents and consolidates their answers, which typically increases token consumption by roughly 3×. In exchange, it scored 50.2% on Humanity's Last Exam without tools, ranking second behind Gemini 3.1 Pro. Use it for scientific, medical, or legal reasoning — not for boilerplate tasks.

Should I self-host Gemma 4 31B?

Only if you process roughly 50,000+ inference tasks per month and you already have or can afford a capable GPU (RTX 4090 or similar). At ~$0.40 per 1M tokens from Together AI, hosted Gemma 4 only becomes more expensive than self-hosting once sustained monthly throughput is high. For bursty or low-volume workloads, just use the hosted API — the flat $300/mo GPU cost kills the math.

Why is Claude Opus 4.6 so expensive?

Claude Opus 4.6 is priced at $15/$75 per 1M input/output tokens because Anthropic positions it as the coding specialist — SWE-bench 80.8% is the highest among major general-purpose models. The calculus is simple: if Claude fixes a bug that would take a human engineer 2 hours, it has already paid for millions of output tokens. Use it for high-stakes code work and fall back to a cheaper model for chat, summarization, or RAG.

How do I reduce my AI costs?

Three levers in order of impact: (1) route cheap/boilerplate tasks to Muse Spark or Gemini 3.1 Pro and reserve Claude for coding; (2) cache prompts with Anthropic prompt caching or OpenAI cached inputs — this can cut input cost by 50-90% on repeated system prompts; (3) self-host Gemma 4 for high-volume internal workflows where latency and privacy matter more than raw quality.

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AI Model Token Cost Calculator

Compare monthly token costs for GPT-5.4, Claude Opus 4.6, Gemini 3.1 Pro, Muse Spark, and self-hosted Gemma 4 31B. Prices approximate as of April 2026.

Prices approximate as of April 2026. Provider pricing changes frequently — verify against official docs before committing.

Step 1 — Your monthly token usage

Monthly input tokens

Tokens you send to the model (prompts, context, docs). 1M input is roughly 750k words.

Monthly output tokens

Tokens generated by the model. Typical ratio is 3-5x input for chat, closer to 1:1 for code.

Step 2 — Primary use case

Step 3 — Modifiers

Muse Spark Contemplating mode

Multiple parallel reasoning agents. ~3x tokens, but ranked #2 on Humanity's Last Exam. Ideal for scientific or medical queries.

Self-host Gemma 4 31B

Replace per-token billing with a flat GPU cost (~$300/mo, RTX 4090). Breakeven around 50k tasks/month.

Results — sorted by monthly cost

#	Model	Monthly	Yearly	Efficiency	Best for	Pick
1	Gemma 4 31B Google (self-host) Open Apache 2.0, 84.3% GPQA Diamond, can self-host on RTX 4090	$0.48	$5.76	65M tokens	CodingGeneral	Best pick
2	Muse Spark Meta Most token-efficient (58M vs Claude 157M), Contemplating mode	$3.00	$36.0	58M tokens	VisionGeneral	Fit
3	Gemini 3.1 Pro Google Multi-task reasoning leader, native multimodal	$5.00	$60.0	58M tokens	ReasoningVision	-
4	GPT-5.4 OpenAI Structured output, tool use, 57/100 benchmark index	$6.00	$72.0	92M tokens	CodingGeneral	Fit
5	Claude Opus 4.6 Anthropic SWE-bench 80.8%, best for coding	$30.0	$360	157M tokens	Coding	-

Cheapest overall

Gemma 4 31B $0.48/mo

Open-weight, self-host saves money at >50k tasks/mo

Best fit for General

Gemma 4 31B $0.48/mo

Open Apache 2.0, 84.3% GPQA Diamond, can self-host on RTX 4090

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About this calculator

In April 2026 the frontier model roster shifted faster than any quarter in the last two years. Gemma 4 dropped under Apache 2.0 on April 2, Llama 4 Scout and Maverick followed on the 5th, and Meta surprised the field on April 8 with Muse Spark and its Contemplating Mode — multiple reasoning agents voting on a single answer at roughly 3x token cost.

This calculator pulls together the five models most developers are actively choosing between, converts your monthly usage into real dollars, and lets you flip the two knobs that change the answer more than anything else: Muse Spark's Contemplating Mode, and whether it makes sense to self-host Gemma 4 on your own GPU.

The Token Efficiency column is worth a second look. Muse Spark and Gemini 3.1 Pro complete the Artificial Analysis Intelligence Index using around 58M tokens, versus 92M for GPT-5.4 and 157M for Claude Opus 4.6. So even when two models have similar per-token prices, the cheaper one on paper can end up 2-3x more expensive per completed task because it generates more scaffolding tokens before arriving at the answer.

How the calculation works

Monthly cost = (input tokens / 1M) x input price + (output tokens / 1M) x output price
Contemplating mode, when enabled, multiplies Muse Spark's cost by 3x
Self-hosting Gemma 4 replaces per-token billing with a flat ~$300/month GPU cost
Efficiency stars = 5 - floor((efficiency tokens - 50M) / 25M), clamped to 1-5

Frequently asked questions

How accurate are these prices?: Approximate as of April 2026 using each provider's public API pricing. For Gemma 4 31B we use Together AI / Fireworks hosted pricing as the baseline. Check each provider's official pricing page before committing — enterprise discounts and prompt caching change the effective rate significantly.
What is Muse Spark's Contemplating Mode?: A multi-agent reasoning feature Meta released on April 8, 2026. Several parallel reasoning agents produce candidate answers which are then consolidated. Token use goes up roughly 3x, accuracy on hard tasks goes up 30-50%. On Humanity's Last Exam (no tools) it scored 50.2%, ranking 2nd behind Gemini 3.1 Pro.
Should I self-host Gemma 4 31B?: Only if you process roughly 50,000+ tasks per month and have a capable GPU. Below that volume, the flat GPU cost (hardware amortization + electricity) dominates the math and hosted APIs win. Above it, Apache 2.0 weights + on-prem latency + privacy start to matter.
Why is Claude Opus 4.6 so expensive?: Anthropic positions Opus 4.6 as the coding specialist — 80.8% on SWE-bench is the highest among general-purpose models. For high-stakes code the math works (a single well-fixed bug pays for a lot of output tokens). For summarization, RAG, or chat, drop down to Gemini 3.1 Pro or Muse Spark.
How do I reduce my AI costs?: In order of impact: (1) route cheap and boilerplate tasks to Muse Spark or Gemini; reserve Claude for code. (2) Use prompt caching (Anthropic, OpenAI, Google all ship a version) — can cut input cost by 50-90% on repeated system prompts. (3) Self-host Gemma 4 for high-volume internal workloads where quality is good enough and privacy matters.

This tool runs entirely in your browser. No tokens, usage data, or API keys are sent anywhere.