ERNIE 5.1 vs Muse Spark 1.3
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | ERNIE 5.1 | Muse Spark 1.3 |
|---|---|---|
| Developer | Baidu | Meta |
| Family | Ernie 5 | Muse Spark |
| Model | ERNIE 5.1 | Muse Spark 1.3 |
| Version | ERNIE 5.1 | Muse Spark 1.3 |
| Lifecycle | active | preview |
| Released | Unknown | 2026-09-02 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Video |
| Output modalities | Text | Text |
| Context window | 131K | Unknown |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Baidu Qianfan (Standard) | Unknown |
| Capabilities | agents, chat, reasoning, search, tools | chat, computer-use, generation, reasoning, research, tools |
11 comparable fields · 7 material differences · Pair passes the primary-source comparison gate
ERNIE 5.1 Capabilities
Muse Spark 1.3 Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
ERNIE 5.1Baidu | vs |  GPT-6 AstraOpenAI | cross-developer peerstext |
Claude Fable 5.1Anthropic | vs | ERNIE 5.1Baidu | cross-developer peerstext |
ERNIE 5.1Baidu | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
ERNIE 5.1Baidu | vs |  DeepSeek-V4-ProDeepSeek | cross-developer peerstext |
ERNIE 5.1Baidu | vs | Grok 4.6xAI | cross-developer peerstext |
ERNIE 5.1Baidu | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
ERNIE 5.1Baidu | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | ERNIE 5.1Baidu | cross-developer peerstext |
ERNIE 5.1Baidu | vs |  GLM-5.3Z.ai | cross-developer peerstext |
ERNIE 5.1Baidu | vs |  Hy4 previewTencent | cross-developer peerstext |
ERNIE 5.1Baidu | vs | Seed 2.1 ProByteDance Seed | cross-developer peerstext |
ERNIE 5.1Baidu | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
ERNIE 5.1 vs Muse Spark 1.3 FAQs
Is ERNIE 5.1 or Muse Spark 1.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both ERNIE 5.1 and Muse Spark 1.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, ERNIE 5.1 or Muse Spark 1.3?+
Only ERNIE 5.1 has a directly sourced input price: $4.00 per million tokens. Only ERNIE 5.1 has a directly sourced output price: $18.00 per million tokens.
Which has a larger context window, ERNIE 5.1 or Muse Spark 1.3?+
Neither model has a larger sourced context window in this comparison. ERNIE 5.1 is 131K and Muse Spark 1.3 is —.
Which performs better in benchmarks, ERNIE 5.1 or Muse Spark 1.3?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can ERNIE 5.1 or Muse Spark 1.3 be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. ERNIE 5.1 is not marked open weight; Muse Spark 1.3 is not marked open weight.
Can ERNIE 5.1 and Muse Spark 1.3 understand images?+
ERNIE 5.1 is not documented with image input; Muse Spark 1.3 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, ERNIE 5.1 or Muse Spark 1.3?+
Neither has a larger sourced maximum output. ERNIE 5.1 is 66K and Muse Spark 1.3 is —.
Do ERNIE 5.1 and Muse Spark 1.3 support reasoning and tool use?+
ERNIE 5.1: reasoning and tool calling. Muse Spark 1.3: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, ERNIE 5.1 or Muse Spark 1.3?+
ERNIE 5.1 has 1 sourced provider route; Muse Spark 1.3 has 0, so ERNIE 5.1 has broader tracked availability.
Which offers better value, ERNIE 5.1 or Muse Spark 1.3?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.