ERNIE X1.1 vs Kimi K2.7 Code

At a Glance

Compare
Kimi K2.7 CodeMoonshot AI
Intelligence, Cost, and Efficiency
CostLower is better · Published-token output estimateUnrankedNot in the 44-model eligible cohort#9 of 44$0.042 per LiveBench case
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.68Deepinfra · Sep 22, 2026
Output priceFrom · USD / 1M tokensNot reported$3.21Openrouter · Sep 22, 2026
Context windowMaximum documented tokens66K262K
Model facts checkedAug 29, 2026View model evidence →Sep 3, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldERNIE X1.1Kimi K2.7 Code
DeveloperBaiduMoonshot AI
FamilyErnie X1Kimi K2 7
ModelERNIE X1.1Kimi K2.7 Code
VersionERNIE X1.1Kimi K2.7 Code
Lifecycleactiveactive
Released2025-09-262026-06-11
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image, Video
Output modalitiesTextText
Context window66K262K
Total parametersUnknown1T
Active parametersUnknown32B
LicenseUnknownmodified-mit
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessBaidu Qianfan (Standard)Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitiesagents, chat, reasoning, search, toolsagents, chat, coding, reasoning, tools, vision

ERNIE X1.1 Capabilities

agentschatreasoningsearchtools
Serving providers1
Canonical IDbaidu/ernie-x1.1

Kimi K2.7 Code Capabilities

agentschatcodingreasoningtoolsvision
Serving providers4
Canonical IDmoonshotai/Kimi-K2.7-Code

Primary Evidence

Sources and Freshness

Questions

ERNIE X1.1 vs Kimi K2.7 Code FAQs

Is ERNIE X1.1 or Kimi K2.7 Code better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both ERNIE X1.1 and Kimi K2.7 Code, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, ERNIE X1.1 or Kimi K2.7 Code?+

ERNIE X1.1 is $1.00 and Kimi K2.7 Code is $0.68 per million tokens, so Kimi K2.7 Code is cheaper on this metric. ERNIE X1.1 is $4.00 and Kimi K2.7 Code is $3.21 per million tokens, so Kimi K2.7 Code is cheaper on this metric.

Which has a larger context window, ERNIE X1.1 or Kimi K2.7 Code?+

Kimi K2.7 Code has the larger sourced context window. ERNIE X1.1 supports 66K and Kimi K2.7 Code supports 262K.

Which performs better in benchmarks, ERNIE X1.1 or Kimi K2.7 Code?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can ERNIE X1.1 or Kimi K2.7 Code be self-hosted?+

Kimi K2.7 Code is the only model in this pair currently marked as self-hostable. ERNIE X1.1 is not marked open weight; Kimi K2.7 Code is open weight.

Can ERNIE X1.1 and Kimi K2.7 Code understand images?+

ERNIE X1.1 is not documented with image input; Kimi K2.7 Code is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, ERNIE X1.1 or Kimi K2.7 Code?+

Neither has a larger sourced maximum output. ERNIE X1.1 is 66K and Kimi K2.7 Code is —.

Do ERNIE X1.1 and Kimi K2.7 Code support reasoning and tool use?+

ERNIE X1.1: reasoning and tool calling. Kimi K2.7 Code: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, ERNIE X1.1 or Kimi K2.7 Code?+

ERNIE X1.1 has 1 sourced provider route; Kimi K2.7 Code has 4, so Kimi K2.7 Code has broader tracked availability.

Which offers better value, ERNIE X1.1 or Kimi K2.7 Code?+

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.

Send Feedback