ERNIE X1.1 vs GLM 5.2

At a Glance

Compare
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5UnrankedNot in the 46-model eligible cohort#23 of 4661.9 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 41.3–74.6
CostLower is better · Published-token output estimateUnrankedNot in the 44-model eligible cohort#12 of 44$0.056 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5UnrankedNot in the 38-model eligible cohort#7 of 3861.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 51.0–67.6
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$0.75Deepinfra · Sep 22, 2026
Output priceFrom · USD / 1M tokensNot reported$2.40Deepinfra · Sep 22, 2026
Context windowMaximum documented tokens66K1,049K
Model facts checkedAug 29, 2026View model evidence →Aug 28, 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 X1.1GLM-5.2
DeveloperBaiduZ.ai
FamilyErnie X1Glm 5 2
ModelERNIE X1.1GLM-5.2
VersionERNIE X1.1GLM-5.2
Lifecycleactiveactive
Released2025-09-26Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesTextText
Context window66K1,049K
Total parametersUnknown753.3B
Active parametersUnknownUnknown
LicenseUnknownmit
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessBaidu Qianfan (Standard)Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitiesagents, chat, reasoning, search, toolschat, generation, reasoning, tools

ERNIE X1.1 Capabilities

agentschatreasoningsearchtools
Serving providers1
Canonical IDbaidu/ernie-x1.1

GLM 5.2 Capabilities

chatgenerationreasoningtools
Serving providers5
Canonical IDzai-org/GLM-5.2

Primary Evidence

Sources and Freshness

Questions

ERNIE X1.1 vs GLM 5.2 FAQs

Is ERNIE X1.1 or GLM 5.2 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both ERNIE X1.1 and GLM 5.2, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, ERNIE X1.1 or GLM 5.2?+

ERNIE X1.1 is $1.00 and GLM 5.2 is $0.75 per million tokens, so GLM 5.2 is cheaper on this metric. ERNIE X1.1 is $4.00 and GLM 5.2 is $2.40 per million tokens, so GLM 5.2 is cheaper on this metric.

Which has a larger context window, ERNIE X1.1 or GLM 5.2?+

GLM 5.2 has the larger sourced context window. ERNIE X1.1 supports 66K and GLM 5.2 supports 1,049K.

Which performs better in benchmarks, ERNIE X1.1 or GLM 5.2?+

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 GLM 5.2 be self-hosted?+

GLM 5.2 is the only model in this pair currently marked as self-hostable. ERNIE X1.1 is not marked open weight; GLM 5.2 is open weight.

Can ERNIE X1.1 and GLM 5.2 understand images?+

ERNIE X1.1 is not documented with image input; GLM 5.2 is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, ERNIE X1.1 or GLM 5.2?+

Neither has a larger sourced maximum output. ERNIE X1.1 is 66K and GLM 5.2 is —.

Do ERNIE X1.1 and GLM 5.2 support reasoning and tool use?+

ERNIE X1.1: reasoning and tool calling. GLM 5.2: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, ERNIE X1.1 or GLM 5.2?+

ERNIE X1.1 has 1 sourced provider route; GLM 5.2 has 5, so GLM 5.2 has broader tracked availability.

Which offers better value, ERNIE X1.1 or GLM 5.2?+

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.

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