Anthropic vs Meta
Meta's new Muse Spark API speaks Anthropic's Messages format at $1.25 in and $4.25 out. It is a cheaper, less proven alternative to Claude, with an even cheaper tier if Meta can train on your data.
By The Subconscious Team · Updated
Anthropic vs Meta: key differences
Meta built its Model API to take traffic from labs like Anthropic. The endpoint speaks Anthropic Messages as well as OpenAI Chat Completions, so switching is mostly configuration. Muse Spark 1.3 targets agentic coding, tool use and computer use with a 1M context at $1.25 in and $4.25 out, near Haiku 4.5's $1 and $5 and far under Fable 5.1's $10 and $50. A Contributor tier drops prices to $0.10 in and $0.20 out if Meta may train on your prompts, with rate limits falling from 3,000 to 100 requests per minute.
Track record is Anthropic's advantage. Meta's API is still in public preview, while Claude has top results on SWE-bench Pro and wide adoption through Claude Code, plus distribution on every major cloud. Meta covers more modalities on one key, with Muse Image at $0.01 per image and transcription at $0.18 per audio hour, and it offers open-weight Muse Glimmer for self-hosting. Keep production coding and sensitive business traffic on Claude. Use Meta for cost-sensitive experiments and multimodal side tasks, and use the Contributor tier only where sharing data is acceptable.
What Anthropic and Meta do
Anthropic
Anthropic sells the Claude family of closed models through its own API, Amazon Bedrock, Google Vertex AI and Microsoft Foundry. The public lineup today runs from Claude Fable 5.1 at the top, released September 1, 2026, through the Opus and Sonnet tiers down to Haiku 4.5. List prices span a tenfold range, from $10 in and $50 out on Fable to $1 in and $5 out on Haiku. The top three tiers include a 1M token context window at standard pricing with no surcharge past 200K.
Example models: Claude Fable 5.1, Claude Haiku 4.5
Full Anthropic profileMeta
Meta has moved from open Llama releases toward its own closed API. Meta Superintelligence Labs builds the Muse family, and in July 2026 Meta opened a public preview of the Meta Model API with Muse Spark 1.1, a multimodal reasoning model aimed at agentic coding, tool use and computer use. The current lineup runs through Muse Spark 1.3 with a 1M token context. Standard pricing is $1.25 in and $4.25 out per million tokens, with cached input at $0.15, and the endpoint speaks OpenAI Chat Completions, Anthropic Messages and a stateful agentic format.
Example models: Muse Spark 1.3, Muse Glimmer
Full Meta profileShould you choose Anthropic or Meta?
Anthropic vs Meta at a glance
| Attribute | ||
|---|---|---|
| Model access | Closed | Closed API; open Muse Glimmer |
| Flagship models | Claude Fable 5.1, Opus, Sonnet, Haiku 4.5 | Muse Spark 1.3, Muse Glimmer |
| Speed | Fable is the slowest tier | ~145–233 tok/s on Muse Spark 1.3 |
| Price | $1–$10 in, $5–$50 out per 1M | $1.25 in, $4.25 out; Contributor tier cheaper |
| Customization | N/A | Open Muse Glimmer weights to fine-tune |
| Deployment | API, Bedrock, Vertex AI, Microsoft Foundry | Meta Model API (preview) |
| Long context | 1M, no surcharge past 200K | 1M |
Frequently asked questions
What is the difference between Anthropic and Meta?
Meta's new Muse Spark API speaks Anthropic's Messages format at $1.25 in and $4.25 out. It is a cheaper, less proven alternative to Claude, with an even cheaper tier if Meta can train on your data.
When should I choose Anthropic over Meta?
Production agents that need a proven API; Traffic that must never be used for training; Top-tier coding quality.
When should I choose Meta over Anthropic?
Cheap prototyping on the Contributor tier; Image generation and transcription on the same key; Mid-tier agentic coding at lower list prices.
Is Anthropic or Meta cheaper?
Anthropic: $1–$10 in, $5–$50 out per 1M. Meta: $1.25 in, $4.25 out; Contributor tier cheaper. The cheaper choice depends on the model and workload.
Which has more context, Anthropic or Meta?
Anthropic: 1M, no surcharge past 200K. Meta: 1M.
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If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.