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

Qwen3.8-Max vs ERNIE 5.1

Qwen3.8-Max wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

VendorAlibaba / Baidu
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose Qwen3.8-Max when coding depth, long-context reliability, lower refusal matter most; choose ERNIE 5.1 when lower cost is the priority.

Choose Qwen3.8-Max if you…

  • Top-tier Chinese
  • Balanced multimodal
  • Alibaba Cloud ecosystem
  • Good value
  • Best for: Chinese tasks, Multimodal, Coding

Choose ERNIE 5.1 if you…

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

Qwen3.8-Max Higher overall
Alibaba · #4 overall
Overall82
Coding77
Multimodal92
VS
ERNIE 5.1
Baidu · #17 overall
Overall66
Coding62
Multimodal74

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.

DimensionQwen3.8-MaxERNIE 5.1Verdict
VendorAlibaba (CN)Baidu (CN)Different vendors
Released2026.072026.05Qwen3.8-Max is newer
Overall (rank)82 · #466 · #17Qwen3.8-Max +16
Coding77 · #662 · #18Qwen3.8-Max +15
Multimodal92 · #274 · #16Qwen3.8-Max +18
Context window1M128KQwen3.8-Max larger
Max output128K32KERNIE 5.1 longer
Effective-context9582Qwen3.8-Max more reliable
Input $/1M$2.5$1.1ERNIE 5.1 cheaper
Output $/1M$7.5$3.3ERNIE 5.1 cheaper
Cache discount80% offnoneQwen3.8-Max deeper
Speed~55 tok/s~45 tok/sQwen3.8-Max faster
TTFT0.6s0.7sQwen3.8-Max snappier
Function calling8878Qwen3.8-Max ahead
Refusal rate~10%~12%Qwen3.8-Max less restrictive
English7865Qwen3.8-Max
Chinese9892Qwen3.8-Max
Modalitiestext, imagetext, imageSame
Open weightsNoNoBoth closed
Fine-tuningYesYes
Free tierFree credits for new Alibaba Cloud Bailiang usersERNIE Bot free; quota on the Qianfan platform
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

Qwen3.8-Max leads the overall aggregate by 16 points (82 vs 66). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while ERNIE 5.1 remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a clear gap: Qwen3.8-Max scores 77 against 62. On multi-file edits, SWE-style tickets and long-horizon agent loops Qwen3.8-Max needs fewer correction turns; ERNIE 5.1 is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Qwen3.8-Max leads multimodal 92 vs 74. Neither emits native video, so the comparison is about parsing images and documents, not generation.

Speed & latency

Qwen3.8-Max is faster in interactive use: ~55 tok/s with 0.6s TTFT versus ~45 tok/s with 0.7s TTFT (about 1.2× 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 1M for Qwen3.8-Max and 128K for ERNIE 5.1. Effective-context scores point the same way as window size — Qwen3.8-Max is ahead on usable recall (95 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 Qwen3.8-Max at $2.5/$7.5 per 1M input/output tokens — list input is about 2.3× lower. Cache discounts (Qwen3.8-Max 80%) shift the effective bill, worked out below.

Chinese vs English

English: Qwen3.8-Max 78 vs ERNIE 5.1 65. Chinese: 98 vs 92. For Chinese-language production, Qwen3.8-Max is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.

Tool use & ecosystem

Function-calling score: Qwen3.8-Max 88 vs ERNIE 5.1 78, so Qwen3.8-Max has the edge on structured tool use. Fine-tuning is available from Qwen3.8-Max and ERNIE 5.1. 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.

Scenario · per monthQwen3.8-MaxERNIE 5.1Gap
List priceno cache applied$475100M in × $2.5  +  30M out × $7.5$209100M in × $1.1  +  30M out × $3.32.27×gap
With caching90% of inputs cache-hit$29590M cached in × $0.5  +  10M in × $2.5  +  30M out × $7.5$209no published cache discount1.41×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 Qwen3.8-Max (coding 77 vs 62).
IF you serve real-time users and latency is a product KPI  →  choose Qwen3.8-Max (~55 tok/s, 0.6s 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 Qwen3.8-Max (effective context 95 vs 82).
07

Frequently asked questions

Is Qwen3.8-Max worth the higher price over ERNIE 5.1?
At list the input rate is 2.3x higher, but cache discounts and output pricing narrow the effective gap. Pay the premium when Qwen3.8-Max's stronger dimensions protect revenue; for routine volume ERNIE 5.1 is the economical pick.
Which is better for agentic coding?
Qwen3.8-Max is decisively stronger for coding (77 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.
How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, Qwen3.8-Max is about $295/month and ERNIE 5.1 about $209/month after cache discounts ($475 and $209 at list).
Does the bigger context window actually matter?
Nominal windows are Qwen3.8-Max (1M) and ERNIE 5.1 (128K), but usable recall follows the effective-context score (95 vs 82). Prefer the higher effective-context model for long-document work where nothing can be missed.
Which handles multimodal inputs better?
Multimodal scores are 92 (Qwen3.8-Max) vs 74 (ERNIE 5.1), with modality coverage text/image versus text/image. Match the model to the input types your product actually receives.
What is the single-line recommendation?
Choose Qwen3.8-Max for Chinese tasks, Multimodal; choose ERNIE 5.1 for Chinese, Search-augmented.