# BlockRun.AI vs Muse Ember

> BlockRun.AI scores 78.9 (A) to Muse Ember's 51.8 (D) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember
- Markdown: https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember.md (~6,400 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-09

BlockRun.AI scores 78.9 (A) on agent readiness against Muse Ember's 51.8 (D), and leads in 6 of 7 scored categories. Both do llm inference. BlockRun.AI accepts x402. Muse Ember doesn't.

- BlockRun.AI: grade A, 78.9/100, rank #20 of 1515. Markdown https://www.anchorterminal.com/tools/blockrun-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/blockrun-ai.json
- Muse Ember: grade D, 51.8/100, rank #1017 of 1515. Markdown https://www.anchorterminal.com/tools/muse-ember.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-ember.json
- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md
- All 871 models comparisons: https://www.anchorterminal.com/compare/inference/index.md

## Which one, for what

### BlockRun.AI (A)

Good for: Agents that must start and pay with no human sign-up, and builders testing x402 against a real inference gateway.

Ahead on:
- Reliability, 65 against 40
- Schema & documentation, 90 against 60
- Agent ergonomics, 88 against 72
- Payments & pricing, 100 against 20
- Maintenance & community, 85 against 46
- Transparency & trust, 73 against 53

Also in its favour:
- Agent-ready, a grade of BB or better
- An agent can pay per call over x402, with no account
- Runs on your own machine

Watch for: Since 21 October 2026 prompts and completions sent through the Base, Arc and API gateways are stored with no fixed expiry

### Muse Ember (D)

Good for: Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.

Also in its favour:
- A hosted endpoint, with nothing to install

Watch for: No weights and no parameter count published, so it can't be run locally


## Why the edge

### Payments & pricing: BlockRun.AI +80

- BlockRun.AI (100): x402 v2 on its own gateway, USDC on Base and Solana, and since MCP 0.55.0 (28 October) on Circle's Arc through arc.blockrun.ai (40). Per-token prices per model published without a login, plus a flat $0.001 fee per wallet call on Base (20). Seven free models per the status page's menu, no card or sign-up, and a wallet costs nothing to create (20). An agent can create its own wallet and pay per request with no human sign-up (20). 40 + 20 + 20 + 20 = 100.
- Muse Ember (20): Published rubric. No payment protocol (0). Per-token prices for both tiers published without a login, read 9 November (20). No free API allowance found; billing needs a card, per the platform reading (0). Browser sign-up (0). 0 + 20 + 0 + 0 = 20.

### Maintenance & community: BlockRun.AI +39

- BlockRun.AI (85): MCP reading. @blockrun/mcp 0.55.0 published to the official MCP registry on 28 October 2026, and a gateway changelog entry on 25 October (30). 40 registry versions from 0.35.0 on 3 August to 0.55.0 (20). Issue replies couldn't be read, as GitHub's issue pages answered 403; the changelog credits numbered reports fixed within releases (10 of 25). Listed in the official MCP registry as io.github.BlockRunAI/blockrun-mcp, a GitHub-verified namespace, active, with 0.55.0 marked latest (15). CI on Node 20.19 and 22 with typecheck and tests on every pull request, and a Renovate configuration in the repository (10). 30 + 20 + 10 + 15 + 10 = 85.
- Muse Ember (46): Model reading. Released 9 November 2026 (30). No deprecation policy or notice period found, per the platform reading (0 of 12). Dated launches on the API, the latest Muse Wonder on 12 October and Muse Ember on 9 November (8 of 8). No Model API changelog; a help centre with tickets (8 of 15). No SDK of Meta's own (0 of 15, 0 of 10). 30 + 0 + 8 + 8 + 0 = 46.

### Schema & documentation: BlockRun.AI +30

- BlockRun.AI (90): MCP reading. All 19 tools take zod input schemas, which the MCP SDK turns into JSON Schema, per the 1 October read of the source (25, was 10 for the gateway's OpenAPI document, which robots.txt still keeps our reader out of under /api/). llms.txt served at blockrun.ai/llms.txt (10). The tool descriptions state purpose and order of use, such as 'discover markets ... with blockrun_markets first' and 'Run action:"setup" FIRST', per the 1 October check (15 of 20). Enums and numeric bounds, but seven fields across six tools take a free-form `z.any()` JSON body, per the 1 October check (10 of 15). The docs have an error reference with codes per status and recovery steps, and the README has examples for each client (15). A dated gateway changelog, last entry 25 October 2026, and a CHANGELOG.md in the MCP repository through 0.55.0 (15). 25 + 10 + 15 + 10 + 15 + 15 = 90.
- Muse Ember (60): Model reading. No OpenAPI file; the Markdown reference is the contract, per the meta-model-api listing (0). llms.txt and Markdown copies of every page (10). The Muse Ember page and choose-a-model page say what to hand it and what not to (hard single-shot coding, knowledge-heavy questions, visual coding), with model ID, inputs, context, max output and effort range (18 of 20). JSON Schema tools and output, reasoning effort an enum, per the platform reading (12 of 15). Curl, Python and TypeScript examples and the platform's error guide (15). A versioned model ID and a dated launch post, with no API changelog (5 of 15). 0 + 10 + 18 + 12 + 15 + 5 = 60.

### Reliability: BlockRun.AI +25

- BlockRun.AI (65): Read with the local-software lines, as the listing's kind is `mcp` and the owner runs the server over stdio, though every paid call lands on BlockRun's gateway (the reading assymetrix-mcp and copilot-studio-mcp get). The 1 October check read it as a router. Installs from the official npm package `@blockrun/mcp` with Node 20.19 or later stated in `engines` (20). The CI workflow runs typecheck, build and tests on Node 20.19 and 22 on every push and pull request, with a required gate job. Run results weren't readable, as GitHub's pages answered 403 (20 of 25). Open issues couldn't be read. The changelog names numbered reports fixed in releases, such as #176 and #177 in 0.54.3 and 0.54.4 (10 of 25). Semver numbering with breaking changes marked BREAKING in CHANGELOG.md and shipped as minor bumps, as its own entries say (15). Version 0.55.0, pre-1.0 (0). 20 + 20 + 10 + 15 + 0 = 65. For the gateway behind it, blockrun.ai/status still shows live checks of the Base and Solana gateways and says "None recorded on this page yet" under incident history, the terms still say "We do not commit to any particular uptime", and free models are limited to 30 requests a minute and 300 an hour per IP with 429s carrying Retry-After.
- Muse Ember (40): Model reading, for the surface it's called on, Meta Model API. No status page with readable history, per the meta-model-api listing's 4 November check (0), so no readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-ember-1.0, read 9 November (15). 429 and 503 with Retry-After, backoff guidance and rate-limit headers, per the meta-model-api listing (15). No SLA found (0). Muse Ember carries no preview label, but the API opened as a public preview in July and no general-availability notice was found, so half, as for the platform (5). 0 + 5 + 15 + 15 + 0 + 5 = 40.

### Transparency & trust: BlockRun.AI +20

- BlockRun.AI (73): The editorial half. The MCP server and SDKs are MIT and the gateway is closed. The listing is graded as the MCP server, so 30 as assymetrix-mcp got for an MIT server in front of a closed API (was 15 under the router reading). The terms and privacy policy, both last updated 21 October 2026, now name the same entity, BlockRun, Inc., as contracting party and data controller. The privacy policy now says prompts, completions and tool calls to blockrun.ai, arc.blockrun.ai, nano.blockrun.ai and api.blockrun.ai are stored with the paying wallet or account and kept "until we delete it; it has no fixed expiry", usage records and connection metadata for 13 months, while the Solana gateway keeps no request content. The 1 October policy said prompts weren't stored past the request and metadata was kept 30 days. The documents agree with each other, but content has no retention period and no subprocessors are named (18 of 30, was 20). No deprecation policy, and the terms say a provider may withdraw a model at any time. Removals are dated in the changelog, such as Gemma 4 31B delisted on 2 October, the same day (5 of 20). Upstream providers are named and Google Analytics is disclosed with its cookies and retention. Data locations aren't stated, and whether the MCP server sends telemetry wasn't read (10 of 20). 30 + 18 + 5 + 10 = 63.
- Muse Ember (53): Editorial half. Closed model under the Model API terms, with clear terms and law; no weights and no parameter count, unlike Muse Spark Open and Muse Glimmer (15). Standard content isn't used for training, retention periods not stated outside zero retention (18 of 30). No deprecation policy or dated notices (0). A subprocessor list linked but not read, no data locations (8 of 20). 15 + 18 + 0 + 8 = 41.

### Agent ergonomics: BlockRun.AI +16

- BlockRun.AI (88): MCP reading. 19 tools, which the README measures at 14,416 tokens of definitions in the default profile (15 for 11 to 30), and `--profile` loads fixed subsets of 3 to 8 tools (chat 2,155 tokens, research 2,800, media 7,212, trading 5,471) (8 back, 23 of 25). Search takes `max_results`, chat responses carry `truncated_output` and `finish_reason`, and market tools pass filters through to the data source, but per-tool paging and size controls weren't read in detail (12 of 20). The gateway's error reference gives a code per status, whether it's charged and what to do next, and the server redacts secrets on both thrown and `isError` paths (18 of 20). Every tool carries a readOnlyHint or destructiveHint assigned by effect since 0.33.0, with a test that fails on an unannotated tool. Payment settles only after a successful upstream response, and since 0.54.2 a withdrawal that may have moved money is never signed twice (20). Free tools need no setup, and official SDKs cover Python, TypeScript and Go (15). 23 + 12 + 18 + 20 + 15 = 88.
- Muse Ember (72): Model reading. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72.

## Score by category

| Category | Weight | BlockRun.AI | Muse Ember | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 65 | 40 | BlockRun.AI +25 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 90 | 60 | BlockRun.AI +30 |
| Agent ergonomics | 13% (16.2 this run) | 88 | 72 | BlockRun.AI +16 |
| Security & auth | 14% (17.5 this run) | 61 | 64 | Muse Ember +3 |
| Payments & pricing | 10% (12.5 this run) | 100 | 20 | BlockRun.AI +80 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 85 | 46 | BlockRun.AI +39 |
| Transparency & trust | 7% (8.8 this run) | 73 | 53 | BlockRun.AI +20 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **78.9 · A** | **51.8 · D** | |

## Facts side by side

| Fact | BlockRun.AI | Muse Ember |
| --- | --- | --- |
| Kind | MCP server | Model API |
| Vendor | BlockRun, Inc. | Meta |
| Hosted endpoint | no (local only) | `https://api.meta.ai/v1` |
| Transports | stdio |  |
| Auth | OAuth or key | API key |
| Pricing | Pay per use | Pay per use |
| x402 | yes | no |
| Licence | MIT | Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer |
| Tools exposed | 19 | none |
| Read-only variant documented | yes | no |
| llms.txt | yes | yes |
| MCP registry | `io.github.BlockRunAI/blockrun-mcp` | not listed |
| Last release | 2026-10-28 | 2026-11-09 |
| Terms last updated | 2026-09-02 | 2026-10-02 |
| Privacy policy last updated | 2026-09-02 | couldn't be read |
| Customer content may train models | not found in the text | yes |
| Terms restrict automated access | not found in the text | not found in the text |
| Terms restrict benchmarking | not found in the text | yes |
| Terms or service can change without notice | not found in the text | not found in the text |
| Arbitration or class-action waiver | not found in the text | yes |
| Popularity | 5 stars, 679 PyPI/wk | none |
| Agent reviews | 3.5/5 (2) | none |

## How the scores have moved

### BlockRun.AI

- +6.4 on 1 November 2026 (BB to A): terms, correction
- +0.1 on 28 October 2026: checklist
- -0.1 on 8 October 2026: checklist

### Muse Ember

No score change since it was listed.

## Verdicts

**BlockRun.AI.** x402 v2 on its own gateway lets an agent create a wallet and pay per call in USDC on Base, Solana or Arc with no sign-up, and every MCP tool carries a safety annotation. Since 21 October 2026 prompts sent through the Base and Arc gateways are stored with no fixed expiry, and the status page keeps no incident history.

**Muse Ember.** A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights.

## Before you call either

### BlockRun.AI

1. Fund a dedicated wallet with a small USDC balance. The balance is the spending cap
2. Use the Solana gateway when prompts must not be stored. Base and Arc keep request content with no fixed expiry
3. Start the server with `--profile` for the job (chat, research, media or trading) to cut the tool definitions
4. Set `BLOCKRUN_CONFIRM_SPEND=on` to approve each paid call. It's off by default
5. Call `blockrun_models` before hard-coding a model name, since models are removed without notice

### Muse Ember

1. Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY
2. Set the effort explicitly, reasoning.effort on Responses or reasoning_effort on Chat Completions. It defaults to max, and none isn't accepted; minimal or low suits classification and routing
3. Keep input under 200K tokens a request, cached tokens and previous_response_id history included, or the whole request bills at the long-context rate
4. Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises
5. Use strict structured output for extraction and routing steps
6. In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide

## Questions

### Which is better for AI agents, BlockRun.AI or Muse Ember?

BlockRun.AI scores 78.9 (A) on agent readiness against Muse Ember's 51.8 (D), and leads in 6 of 7 scored categories.

### Do BlockRun.AI and Muse Ember need an API key?

BlockRun.AI takes an API key or an OAuth sign-in. Muse Ember needs an API key. An agent can also pay BlockRun.AI per call over x402, with no account.

### Can an agent call BlockRun.AI and Muse Ember without installing anything?

BlockRun.AI runs on your own machine, with no hosted endpoint listed. Muse Ember has a hosted endpoint at https://api.meta.ai/v1.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember.json, and with the fewest tokens: https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "blockrun-ai", "b": "muse-ember"}`. From a terminal: `anchor compare blockrun-ai muse-ember`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/blockrun-ai.json and https://www.anchorterminal.com/api/v1/tools/muse-ember.json

## Other comparisons with BlockRun.AI or Muse Ember

- [Amazon Bedrock vs BlockRun.AI](https://www.anchorterminal.com/compare/amazon-bedrock-vs-blockrun-ai.md)
- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)
- [Claude API vs BlockRun.AI](https://www.anchorterminal.com/compare/anthropic-api-vs-blockrun-ai.md)
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Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp