MModelspectraIndependent AI Model Intelligence
Head-to-head · Updated 2026-09-08
Model Comparison · Head to Head

ERNIE 5.1 vs Muse Spark 1.1

ERNIE 5.1 wins on Overall, Coding; Muse Spark 1.1 wins on Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

VendorBaidu / Meta
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose ERNIE 5.1 when coding depth, long-context reliability matter most; choose Muse Spark 1.1 when lower cost is the priority.

Choose ERNIE 5.1 if you…

  • Optimized for Chinese
  • Search augmentation
  • Baidu integration
  • Mature enterprise services
  • Best for: Chinese, Search-augmented, Enterprise services

Choose Muse Spark 1.1 if you…

  • Completely free open weights
  • By Meta
  • Self-hostable
  • Multimodal
  • Best for: Open research, Local deployment, Experimental
02

Head-to-head aggregate scores

Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.

ERNIE 5.1 Higher overall
Baidu · #17 overall
Overall66
Coding62
Multimodal74
VS
Muse Spark 1.1
Meta · #22 overall
Overall61
Coding59
Multimodal75

Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.

03

Specs & pricing — every field side by side

List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.

DimensionERNIE 5.1Muse Spark 1.1Verdict
VendorBaidu (CN)Meta (US)Different vendors
Released2026.052026.08Muse Spark 1.1 is newer
Overall (rank)66 · #1761 · #22ERNIE 5.1 +5
Coding62 · #1859 · #21ERNIE 5.1 +3
Multimodal74 · #1675 · #15Muse Spark 1.1 +1
Context window128K256KMuse Spark 1.1 larger
Max output32K64KMuse Spark 1.1 longer
Effective-context8280ERNIE 5.1 more reliable
Input $/1M$1.1FreeMuse Spark 1.1 cheaper
Output $/1M$3.3FreeMuse Spark 1.1 cheaper
Cache discountnonenoneTie
Speed~45 tok/sHardware-dependentERNIE 5.1 faster
TTFT0.7sHardware-dependentERNIE 5.1 snappier
Function calling7865ERNIE 5.1 ahead
Refusal rate~12%~4%Muse Spark 1.1 less restrictive
English6578Muse Spark 1.1
Chinese9260ERNIE 5.1
Modalitiestext, imagetext, imageSame
Open weightsNoYesMuse Spark 1.1 is open
Fine-tuningYesYes
Free tierERNIE Bot free; quota on the Qianfan platformFully free (model weights)
SOC2 / no-trainno / yesno / yes
Private deploymentYesYesBoth support it

Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.

04

Dimension-by-dimension analysis

Reasoning & overall intelligence

ERNIE 5.1 leads the overall aggregate by 5 points (66 vs 61). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while Muse Spark 1.1 remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a modest gap: ERNIE 5.1 scores 62 against 59. On multi-file edits, SWE-style tickets and long-horizon agent loops ERNIE 5.1 needs fewer correction turns; Muse Spark 1.1 is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Muse Spark 1.1 leads multimodal 75 vs 74. Neither emits native video, so the comparison is about parsing images and documents, not generation.

Speed & latency

Muse Spark 1.1 is self-hosted, so its speed depends on your hardware; against a managed API like ERNIE 5.1 (~45 tok/s, 0.7s TTFT) compare on your own infrastructure before assuming latency.

Context: window vs usable recall

Nominal windows are 128K for ERNIE 5.1 and 256K for Muse Spark 1.1. Crucially, the larger nominal window does not win on usable recall: Muse Spark 1.1 advertises 256K but ERNIE 5.1 scores higher on effective-context (82 vs 80), i.e. it actually retains more of what it was given.

Price & total cost

Muse Spark 1.1 is free open weights (you pay only for the infrastructure you run it on), while ERNIE 5.1 is a paid API at $1.1/$3.3 per 1M tokens. The real comparison is total cost of ownership — GPU/ops against a managed bill — not list price alone.

Chinese vs English

English: ERNIE 5.1 65 vs Muse Spark 1.1 78. Chinese: 92 vs 60. For Chinese-language production, ERNIE 5.1 is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.

Tool use & ecosystem

Function-calling score: ERNIE 5.1 78 vs Muse Spark 1.1 65, so ERNIE 5.1 has the edge on structured tool use. Fine-tuning is available from ERNIE 5.1 and Muse Spark 1.1. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.

05

Cost worked example — same workload, real token math

Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.

Cost note: one of these models is free open weights, so a per-token monthly bill does not apply — budget instead for GPU and operations. The paid API counterpart works out to roughly $209/month at list for this workload.

Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.

06

Decision tree

IF the workload is agentic or multi-file coding and a wrong first pass is expensive  →  choose ERNIE 5.1 (coding 62 vs 59).
IF you serve real-time users and latency is a product KPI  →  choose ERNIE 5.1 (~45 tok/s, 0.7s TTFT).
IF you need free, self-hostable weights and can run your own GPU/ops  →  choose Muse Spark 1.1 (free open weights).
IF long-document recall has to be near-perfect  →  choose ERNIE 5.1 (effective context 82 vs 80).
07

Frequently asked questions

Does ERNIE 5.1's higher refusal rate matter in production?
ERNIE 5.1 refuses about 12% of prompts versus 4% for Muse Spark 1.1. In unattended pipelines that means more retries, fallbacks and manual review, raising effective cost and latency even when the token price is lower.
Does the bigger context window actually matter?
Nominal windows are ERNIE 5.1 (128K) and Muse Spark 1.1 (256K), but usable recall follows the effective-context score (82 vs 80). Prefer the higher effective-context model for long-document work where nothing can be missed.
How do they differ for Chinese-language and data-residency use?
ERNIE 5.1 is the Chinese model (Chinese score 92, domestic cloud, possible private deployment) while Muse Spark 1.1 is the global model (Chinese 60, overseas API). Pick by language quality, access path and where data must reside.
Can I self-host either model?
Muse Spark 1.1 ships open weights and can be self-hosted (GPU permitting) for data control; ERNIE 5.1 is a closed managed API with no self-hosting. Choose open weights when residency or cost-at-scale dominates, managed API for convenience.
What is the single-line recommendation?
Choose ERNIE 5.1 for Chinese, Search-augmented; choose Muse Spark 1.1 for Open research, Local deployment.
How quickly do these rankings change?
Modelspectra refreshes the aggregate as new public benchmarks and prices appear. Treat scores within 3 points as a tie and re-check before a committed purchase.