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

Kimi K3 vs ERNIE 5.1

Kimi K3 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.

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

Verdict at a glance

Bottom line: Choose Kimi K3 when coding depth, long-context reliability, lower refusal matter most; choose ERNIE 5.1 when lower cost, fine-tuning/ecosystem is the priority.

Choose Kimi K3 if you…

  • Best-in-class long text
  • Open-source and self-hostable
  • Strong codebase understanding
  • Fair price
  • Best for: Long-document reading, Codebase analysis, Chinese

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.

Kimi K3 Higher overall
Moonshot AI · #8 overall
Overall77
Coding71
Multimodal81
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.

DimensionKimi K3ERNIE 5.1Verdict
VendorMoonshot AI (CN)Baidu (CN)Different vendors
Released2026.062026.05Kimi K3 is newer
Overall (rank)77 · #866 · #17Kimi K3 +11
Coding71 · #1162 · #18Kimi K3 +9
Multimodal81 · #1074 · #16Kimi K3 +7
Context window256K128KKimi K3 larger
Max output64K32KKimi K3 longer
Effective-context9982Kimi K3 more reliable
Input $/1M$3$1.1ERNIE 5.1 cheaper
Output $/1M$15$3.3ERNIE 5.1 cheaper
Cache discountcustomnoneERNIE 5.1 deeper
Speed~45 tok/s~45 tok/sTie
TTFT0.7s0.7sTie
Function calling8078Kimi K3 ahead
Refusal rate~6%~12%Kimi K3 less restrictive
English7565Kimi K3
Chinese9292Tie
Modalitiestext, imagetext, imageSame
Open weightsYesNoKimi K3 is open
Fine-tuningNoYes
Free tierKimi App free; free quota on the open platformERNIE 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

Kimi K3 leads the overall aggregate by 11 points (77 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 modest gap: Kimi K3 scores 71 against 62. On multi-file edits, SWE-style tickets and long-horizon agent loops Kimi K3 needs fewer correction turns; ERNIE 5.1 is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Kimi K3 leads multimodal 81 vs 74. Neither emits native video, so the comparison is about parsing images and documents, not generation.

Speed & latency

Kimi K3 is faster in interactive use: ~45 tok/s with 0.7s TTFT versus ~45 tok/s with 0.7s TTFT (about 1.0× 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 256K for Kimi K3 and 128K for ERNIE 5.1. Effective-context scores point the same way as window size — Kimi K3 is ahead on usable recall (99 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 Kimi K3 at $3/$15 per 1M input/output tokens — list input is about 2.7× lower.

Chinese vs English

English: Kimi K3 75 vs ERNIE 5.1 65. Chinese: 92 vs 92. For Chinese-language production, Kimi K3 is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.

Tool use & ecosystem

Function-calling score: Kimi K3 80 vs ERNIE 5.1 78, so Kimi K3 has the edge on structured tool use. Fine-tuning is available from 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 monthKimi K3ERNIE 5.1Gap
List priceno cache applied$750100M in × $3  +  30M out × $15$209100M in × $1.1  +  30M out × $3.33.59×gap
With caching90% of inputs cache-hit$750no published cache discount$209no published cache discount3.59×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 Kimi K3 (coding 71 vs 62).
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 Kimi K3 (effective context 99 vs 82).
IF data residency or air-gapped deployment is a hard requirement  →  choose Kimi K3 (self-hostable open weights).
07

Frequently asked questions

Is Kimi K3 worth the higher price over ERNIE 5.1?
At list the input rate is 2.7x higher, but cache discounts and output pricing narrow the effective gap. Pay the premium when Kimi K3's stronger dimensions protect revenue; for routine volume ERNIE 5.1 is the economical pick.
Does ERNIE 5.1's higher refusal rate matter in production?
ERNIE 5.1 refuses about 12% of prompts versus 6% for Kimi K3. 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, Kimi K3 is about $750/month and ERNIE 5.1 about $209/month after cache discounts ($750 and $209 at list).
Does the bigger context window actually matter?
Nominal windows are Kimi K3 (256K) and ERNIE 5.1 (128K), but usable recall follows the effective-context score (99 vs 82). Prefer the higher effective-context model for long-document work where nothing can be missed.
Can I self-host either model?
Kimi K3 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.
Which one supports fine-tuning?
ERNIE 5.1 supports fine-tuning; Kimi K3 does not at this tier. If you plan to adapt the model to a narrow domain, that is a concrete differentiator.