GPT-5 nano (high) vs Muse Spark 1.1 (xhigh): The Ultimate Performance & Pricing Comparison

Muse Spark 1.1 is the more defensible choice for capability-led agent development, while GPT-5 nano is the safer cost hypothesis rather than a confirmed production option.

The available data gives Muse Spark 1.1 a 50.6 Artificial Analysis Intelligence Index and a 71.3 Coding Index. GPT-5 nano records a 19.9 Intelligence Index and an 83.7 Math Index. These scores do not establish a complete head-to-head winner because the models do not share a fully identical evaluation set. Data provided by Artificial Analysis

The larger issue is model identity. OpenAI's current model directory does not list GPT-5 nano, gpt-5-nano, or gpt-5-nano-2025-08-07. It therefore does not confirm the model's current API name, limits, or availability. OpenAI Models

Muse Spark 1.1 has a documented API identifier, muse-spark-1.1, and Meta describes it as a public preview available to developers in the United States. Meta developer guide

Muse Spark 1.1 offers stronger broad capability evidence, but GPT-5 nano has the clearer numerical cost advantage.

The capability comparison is asymmetric. Muse Spark 1.1 has a reported coding score, while GPT-5 nano has a reported math score. Neither missing value should be treated as zero. The Artificial Analysis snapshot provides the comparison data, but it does not make the two models directly interchangeable across every workload. Data provided by Artificial Analysis

Meta documents Muse Spark 1.1 for tool calling, computer use, coding, multimodal understanding, and agent tasks. Meta announcement

OpenAI's current model documentation gives only a general overview of recent models and does not specifically document GPT-5 nano. OpenAI Models

For a new application, this creates an unusual decision: Muse Spark has more usable product evidence, while GPT-5 nano has more attractive economics but weaker current documentation evidence.

Muse Spark 1.1 is the stronger broad-task candidate in the available evidence, while GPT-5 nano remains a narrowly promising math option.

The Intelligence Index is 50.6 for Muse Spark 1.1 and 19.9 for GPT-5 nano. That gap suggests a meaningful difference for developers evaluating general reasoning, planning, and mixed-task behavior, but the snapshot does not provide enough detail to identify which individual tasks drive the gap. Data provided by Artificial Analysis

GPT-5 nano's 83.7 Math Index is important because it prevents a simple “Muse Spark wins everything” conclusion. A developer building constrained mathematical transformations, arithmetic-heavy validation, or another workload aligned with that evaluation may still want to test GPT-5 nano first. The brief does not provide a Muse Spark math score, so it cannot establish a math winner. Data provided by Artificial Analysis

Muse Spark 1.1 also has a reported Coding Index of 71.3. That makes it the only model in this comparison with a supplied coding score, but it does not prove superiority over GPT-5 nano because GPT-5 nano's corresponding value is missing. Data provided by Artificial Analysis

The equal latency value of 0.3 seconds suggests that initial response delay should not decide between them. Muse Spark's 166.23 median output tokens per second may matter more for long streamed answers, yet GPT-5 nano has no reported output-speed value. Data provided by Artificial Analysis

Meta positions Muse Spark around parallel tool calls, structured tool use, web-search grounding, and context continuity. Meta developer guide Those features could matter more than benchmark gaps for an agent that spends much of its time selecting tools and managing state.

Evidence is insufficient for a reliable claim about production throughput, error rates, or large-project coding stability. The community material does not disclose a sufficiently rigorous speed or large-codebase test. Reddit discussion

GPT-5 nano is dramatically cheaper in the supplied data, but its undocumented current status can turn a low unit price into an unusable option.

GPT-5 nano costs $0.1375 per 1M blended tokens, compared with $2 for Muse Spark 1.1. Its listed input price is $0.05 versus $1.25, and its output price is $0.4 versus $4.25. Data provided by Artificial Analysis

The practical implication depends on token mix. Output-heavy workloads expose the larger pricing difference because Muse Spark's output price is $4.25 per 1M tokens. Long agent traces, generated code, and verbose structured responses may therefore cost more than a simple blended estimate suggests. The exact workload cost still depends on actual input and output volumes.

Muse Spark's pricing is documented by Meta, which also says new accounts receive a one-time $20 testing credit. Meta developer guide That credit lowers the cost of an initial evaluation, but it does not remove preview, regional, or capacity risk. Meta's documentation says Model API availability can depend on the user's region. Model API overview

OpenAI's current pricing page does not list gpt-5-nano. It lists gpt-5.4-nano at $0.20 input, $0.02 cached input, and $1.25 output per 1M tokens, but those values must not be transferred to GPT-5 nano. OpenAI pricing

This is the key cost caveat: GPT-5 nano is cheaper in the supplied comparison, but the current official pages do not confirm that the compared model can still be called under a stable public identifier. A paid proof of concept should verify that before forecasting savings.

Muse Spark 1.1 is the better default for a new agent or multimodal developer project, while GPT-5 nano deserves a targeted validation only if its endpoint is confirmed.

Pick Muse Spark 1.1 when your application needs tool calling, computer-use patterns, image, video, or PDF inputs. Meta documents those capabilities and supports OpenAI SDK-compatible Chat Completions and Responses API integrations. Model API models Meta developer guide

Pick GPT-5 nano when your primary requirement is very low token cost and your workload is math-oriented. The supplied data reports an 83.7 Math Index and a $0.1375 blended price per 1M tokens. Data provided by Artificial Analysis This recommendation remains conditional because OpenAI's current model directory does not list GPT-5 nano. OpenAI Models

Treat Muse Spark's xhigh label as a reasoning setting, not as a separate model. Meta documents reasoning_effort levels from minimal through xhigh, and says the model always reasons. Reasoning documentation Developers should budget for that behavior rather than assume a no-reasoning mode is available.

Avoid choosing Muse Spark solely for creative conversation. A non-standard Reddit roleplay test reported weak character consistency and emotional understanding, while a Hacker News discussion described repetitive voice patterns in generated applications. Reddit roleplay test Hacker News discussion These reports are directional, not controlled benchmarks.

The safest selection process is a small task-specific bake-off. Test tool selection, structured outputs, math cases, coding changes, long conversations, and failure recovery. The supplied material does not provide enough evidence to predict your application's success rate from benchmark values alone.

Muse Spark 1.1 is easier to evaluate as a real product because Meta publishes an API identifier and usage documentation.

The central unresolved question is whether GPT-5 nano remains callable under a stable public endpoint. OpenAI's current model directory and pricing page do not confirm it, so developers should verify access before designing around its supplied economics. OpenAI Models OpenAI pricing

A second unresolved question is benchmark comparability. The supplied snapshot reports different evaluation families for the two models, including a Math Index only for GPT-5 nano and a Coding Index only for Muse Spark 1.1. Data provided by Artificial Analysis That evidence supports workload-specific testing, not a universal ranking.

A third unresolved question is production readiness. Muse Spark 1.1 is documented as public preview, and the supplied material does not identify an independent SLA for this model. Meta model page A preview can be appropriate for experimentation, but a production team should confirm regional access, limits, and operational behavior.

Developers should also decide whether they need reasoning summaries or hidden internal reasoning. Meta's documentation says Chat Completions returns final output behavior, while Responses API supports the relevant multi-turn workflow. Chat Completions documentation Meta developer guide

These gaps are not minor documentation details. They directly affect model routing, cost forecasts, observability, and rollback planning.