Meta has 3 models in The AI Leaderboard directory: Muse Spark 1.3, Muse Spark 1.1, Llama 4. This provider hub compares their verified availability, licensing, context windows, pricing references and intended uses, with direct links to every model guide.
Quick answer: Muse Spark 1.3 is Meta's highest-ranked model here at #3 with an Overall score of 81.6. Use the lineup table below to compare it with the provider’s other current, specialist, open-weight or migration-reference models.
Meta model lineup at a glance
Metric | Current directory value |
|---|---|
Models covered | 3 |
Currently ranked | 1 |
Open-weight models | 1 |
Restricted models | 0 |
Legacy or migration references | 0 |
Highest-ranked model | Muse Spark 1.3, #3, Overall 81.6 |
Last verified | September 3, 2026 |
Complete Meta model directory
Model names in this table link directly to their complete guides. Status and pricing can change, so confirm the active endpoint and provider terms before procurement or migration.
Model | Status | License | Context | Best fit |
|---|---|---|---|---|
Available through Muse Code and Meta Model API; max reasoning was still pending additional safety testing at launch | Proprietary hosted model | 1 million tokens | long-horizon agentic workflows, coding, multimodal analysis and tool-assisted professional work | |
Available; open-weight Scout and Maverick | Open weights under the Llama license | Up to 10 million tokens for Scout; variant-specific for Maverick | open multimodal deployment, long-context research and custom private systems | |
Available through Meta Model API access | Proprietary hosted model | 1 million tokens | agentic coding, interface debugging and long-context multimodal work |
How the Meta models differ
Model | Directory role | Modalities | Maximum output | Access |
|---|---|---|---|---|
Muse Spark 1.3 | Available proprietary | Text, image, video and document input; text and tool-directed output | Endpoint-specific within the documented context budget; verify the active Meta Model API limit | Muse Code and Meta Model API |
Llama 4 | Open weights | Native text and image input; text output | Variant and host specific | Meta-hosted products, downloadable weights and third-party inference providers |
Muse Spark 1.1 | Available proprietary | Multimodal input; text and tool-directed output | Check Meta Model API limits for the active endpoint | Meta Model API and approved integrations |
The labels above describe access and lifecycle, not a hidden capability ranking. Only 1 Meta model in the current top eight receive a rank on this site. Other guides remain unranked until comparable evidence is available.
Meta model pricing
Model | Pricing reference | Cost caveat |
|---|---|---|
Muse Spark 1.3 | Standard endpoint: $1.25 input, $0.15 cached input and $4.25 output per million tokens; contributor endpoint: $0.10 input, $0.002 cached input and $0.20 output | Caching, tools, long context and regional rates may differ |
Llama 4 | No Meta token fee for weights; hosting and provider rates vary | Self-hosting still incurs infrastructure and operations costs |
Muse Spark 1.1 | Meta Model API pricing varies by access plan; verify the active console rate | Caching, tools, long context and regional rates may differ |
Token price is not cost per accepted result. Measure retries, tool calls, latency, infrastructure and reviewer time on the same task set.
Which Meta model should you choose?
Requirement | Starting point | Why |
|---|---|---|
Highest current rank | Muse Spark 1.3 | Highest Meta placement in the current top eight |
Open weights or self-hosting | Llama 4 | Review the exact license, checkpoint and hardware needs |
Restricted specialist work | No restricted model in this provider directory | Use generally available models and least-privilege controls |
Migration or reproduction | Muse Spark 1.1 | Compare current versions using pinned snapshots |
Lowest operational cost | Measure two or three eligible models | Headline token prices omit retries, tools, caching and human review |
Meta models, explained
Muse Spark 1.3
Muse Spark 1.3 was released September 3, 2026. It is listed as available through muse code and meta model api; max reasoning was still pending additional safety testing at launch with proprietary hosted model. The documented context window is 1 million tokens, and the maximum output is Endpoint-specific within the documented context budget; verify the active Meta Model API limit.
- Best fit: long-horizon agentic workflows, coding, multimodal analysis and tool-assisted professional work.
- Modalities: Text, image, video and document input; text and tool-directed output.
- Access: Muse Code and Meta Model API.
- Pricing reference: Standard endpoint: $1.25 input, $0.15 cached input and $4.25 output per million tokens; contributor endpoint: $0.10 input, $0.002 cached input and $0.20 output.
- Current ranking: #3 with an Overall score of 81.6.
Read the complete Muse Spark 1.3 guide for benchmarks, limitations, official sources and an evaluation framework.
Llama 4
Llama 4 was released April 5, 2025. It is listed as available; open-weight scout and maverick with open weights under the llama license. The documented context window is Up to 10 million tokens for Scout; variant-specific for Maverick, and the maximum output is Variant and host specific.
- Best fit: open multimodal deployment, long-context research and custom private systems.
- Modalities: Native text and image input; text output.
- Access: Meta-hosted products, downloadable weights and third-party inference providers.
- Pricing reference: No Meta token fee for weights; hosting and provider rates vary.
Read the complete Llama 4 guide for benchmarks, limitations, official sources and an evaluation framework.
Muse Spark 1.1
Muse Spark 1.1 was released July 9, 2026. It is listed as available through meta model api access with proprietary hosted model. The documented context window is 1 million tokens, and the maximum output is Check Meta Model API limits for the active endpoint.
- Best fit: agentic coding, interface debugging and long-context multimodal work.
- Modalities: Multimodal input; text and tool-directed output.
- Access: Meta Model API and approved integrations.
- Pricing reference: Meta Model API pricing varies by access plan; verify the active console rate.
Read the complete Muse Spark 1.1 guide for benchmarks, limitations, official sources and an evaluation framework.
How to evaluate Meta models
Shortlist the Meta models that meet your access, licensing and modality requirements. Freeze 20 to 50 representative tasks, then give each candidate the same prompts, tools, context, reasoning budget and retry limits. Record the exact model identifier because provider aliases can change.
Measure | Record | Decision use |
|---|---|---|
Task completion | Pass rate and rubric score | Establish whether the model meets the acceptance threshold |
Reliability | Repeated-run variance and recovery behavior | Avoid choosing from one lucky result |
Quality | Factuality, instruction following and reviewer acceptance | Separate polished output from correct output |
Tool use | Wrong calls, retries, permission errors and unsafe actions | Evaluate the model and agent harness together |
Efficiency | Wall time, tokens, caching, tools and review labor | Calculate cost per accepted result |
Governance | Retention, training, regional processing and audit logs | Confirm the deployment fits policy requirements |
Availability, versions and lifecycle
Meta may expose different snapshots, effort settings, products and partner integrations under similar labels. Treat every model name as incomplete without an exact endpoint or checkpoint. Re-run regression tests after any model, system prompt, retrieval, tool or permission change.
- Available means a documented access route exists, not that every region or account has access.
- Preview models can change behavior, price or retirement dates before general availability.
- Open weights require an exact license and checkpoint review; “open” does not guarantee open training data or low operating cost.
- Restricted models require separate eligibility, safeguards and data-handling review.
- Legacy models remain useful for migration and reproduction but should not be the default for a new deployment.
Sources and update policy
Official Meta model documentation is the provider-level starting point. Each linked model guide cites its own first-party launch page, model card, pricing reference and safety documentation.
Compare this provider with the complete Best AI Models directory.
Last verified September 3, 2026. The hub should be updated when the provider launches, retires, renames or reprices a model, changes context or output limits, alters licensing, or modifies regional availability.
Frequently asked questions
How many Meta models are covered?
This hub covers 3: Muse Spark 1.3, Muse Spark 1.1, Llama 4. Every model has a dedicated guide linked from the directory table and profile sections.
What is the best Meta model?
Muse Spark 1.3 is Meta's highest-ranked model in the current AI Leaderboard, at number 3 with an Overall score of 81.6. Workload-specific testing can still favor another model.
Does Meta offer open weights?
Yes. The directory identifies Llama 4 as open-weight options. Review the exact license, downloadable artifact and serving requirements before deployment.
How current is the pricing?
Pricing references were verified September 3, 2026 from first-party documentation summarized in each model guide. Always recheck the provider before budgeting because cache, batch, long-context, tool and regional rates can change.
Should every Meta model be tested?
No. Remove models that fail access, license, modality, context, latency or governance requirements first. Test the smallest credible shortlist on representative work.