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

ERNIE 5.1 vs Muse Spark 1.3

ERNIE 5.1 wins on Overall; Muse Spark 1.3 wins on Coding, 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 its stronger dimensions matter most; choose Muse Spark 1.3 when lower latency 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 1.3 if you…

  • #1 DeepSWE long-horizon coding
  • Open weights, self-hostable
  • Extremely low cost
  • Fast
  • Best for: Coding, Open-source deployment, Budget
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 26 tracked models.

ERNIE 5.1 Higher overall
Baidu · #20 overall
Overall66
Coding62
Multimodal74
VS
Muse Spark 1.3
Meta · #22 overall
Overall64
Coding88
Multimodal76

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 1.3Verdict
VendorBaidu (CN)Meta (US)Different vendors
Released2026.052026.09Muse Spark 1.3 is newer
Overall (rank)66 · #2064 · #22ERNIE 5.1 +2
Coding62 · #2288 · #5Muse Spark 1.3 +26
Multimodal74 · #2076 · #17Muse Spark 1.3 +2
Context window128K256KMuse Spark 1.3 larger
Max output32K64KMuse Spark 1.3 longer
Effective-context8285Muse Spark 1.3 more reliable
Input $/1M$1.1$1.25ERNIE 5.1 cheaper
Output $/1M$3.3$4.25ERNIE 5.1 cheaper
Cache discountnonenoneTie
Speed~45 tok/s~70 tok/sMuse Spark 1.3 faster
TTFT0.7s0.4sMuse Spark 1.3 snappier
Function calling7872ERNIE 5.1 ahead
Refusal rate~12%~4%Muse Spark 1.3 less restrictive
English6584Muse Spark 1.3
Chinese9262ERNIE 5.1
Modalitiestext, imagetext, imageSame
Open weightsNoYesMuse Spark 1.3 is open
Fine-tuningYesYes
Free tierERNIE Bot free; quota on the Qianfan platformOpen weights free to self-host; hosted API at $1.25/$4.25 per 1M
SOC2 / no-trainno / yesno / yes
Private deploymentYesYesBoth support it

Fields drawn from vendor public documentation and the Modelspectra 26-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 2 points (66 vs 64). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while Muse Spark 1.3 remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a decisive gap: Muse Spark 1.3 scores 88 against 62. On multi-file edits, SWE-style tickets and long-horizon agent loops Muse Spark 1.3 needs fewer correction turns; ERNIE 5.1 is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Muse Spark 1.3 leads multimodal 76 vs 74. Neither emits native video, so the comparison is about parsing images and documents, not generation.

Speed & latency

Muse Spark 1.3 is faster in interactive use: ~70 tok/s with 0.4s TTFT versus ~45 tok/s with 0.7s TTFT (about 1.6× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.

Context: window vs usable recall

Nominal windows are 128K for ERNIE 5.1 and 256K for Muse Spark 1.3. Effective-context scores point the same way as window size — Muse Spark 1.3 is ahead on usable recall (85 vs 82), so prefer it for long-document work where details cannot be missed.

Price & total cost

ERNIE 5.1 is the cheaper API at $1.1/$3.3 versus Muse Spark 1.3 at $1.25/$4.25 per 1M input/output tokens — list input is about 1.1× lower.

Chinese vs English

English: ERNIE 5.1 65 vs Muse Spark 1.3 84. Chinese: 92 vs 62. 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 1.3 72, so ERNIE 5.1 has the edge on structured tool use. Fine-tuning is available from ERNIE 5.1 and Muse Spark 1.3. 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.

Scenario · per monthERNIE 5.1Muse Spark 1.3Gap
List priceno cache applied$209100M in × $1.1  +  30M out × $3.3$252100M in × $1.25  +  30M out × $4.251.21×gap
With caching90% of inputs cache-hit$209no published cache discount$252no published cache discount1.21×gap

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 Muse Spark 1.3 (coding 88 vs 62).
IF you serve real-time users and latency is a product KPI  →  choose Muse Spark 1.3 (~70 tok/s, 0.4s TTFT).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose ERNIE 5.1 ($$1.1/$$3.3 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose Muse Spark 1.3 (effective context 85 vs 82).
07

Frequently asked questions

Which is better for agentic coding?
Muse Spark 1.3 is decisively stronger for coding (88 vs 62 on the aggregate). The gap shows on SWE-style multi-file tasks and long agent loops that need fewer correction turns; ERNIE 5.1 is fine for routine scripts.
Does ERNIE 5.1's higher refusal rate matter in production?
ERNIE 5.1 refuses about 12% of prompts versus 4% for Muse Spark 1.3. In unattended pipelines that means more retries, fallbacks and manual review, raising effective cost and latency even when the token price is lower.
How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, ERNIE 5.1 is about $209/month and Muse Spark 1.3 about $252/month after cache discounts ($209 and $252 at list).
Does the bigger context window actually matter?
Nominal windows are ERNIE 5.1 (128K) and Muse Spark 1.3 (256K), but usable recall follows the effective-context score (82 vs 85). 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 1.3 is the global model (Chinese 62, overseas API). Pick by language quality, access path and where data must reside.
Can I self-host either model?
Muse Spark 1.3 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.