Muse Spark 1.2 vs GLM 5.1

At a Glance

Compare
Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$1.05Deepinfra · Sep 22, 2026
Output priceFrom · USD / 1M tokensNot reported$3.50Deepinfra · Sep 22, 2026
Context windowMaximum documented tokensNot reported203K
Model facts checkedSep 3, 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 →
BenchmarkMuse Spark 1.2GLM-5.1
LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader1,489.44100% of row best · rating · muse-spark-1.2 (xHigh); 95% CI [1478.95697715, 1499.92080617]; votes 3227; rank 111,462.3898% of row best · rating · glm-5.1; 95% CI [1458.52340519, 1466.22702609]; votes 48901; rank 33
Overall ResultCounted from the protocol-matched rows above1 benchmark winNo overall winner0 benchmark winsNo overall winner

Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.

Side-by-Side Facts

FieldMuse Spark 1.2GLM-5.1
DeveloperMetaZ.ai
FamilyMuse SparkGlm 5 1
ModelMuse Spark 1.2GLM-5.1
VersionMuse Spark 1.2GLM-5.1
Lifecyclepreviewactive
Released2026-08-05Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Video, AudioText
Output modalitiesTextText
Context windowUnknown203K
Total parametersUnknown753.9B
Active parametersUnknownUnknown
LicenseUnknownmit
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessUnknownDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitieschat, computer-use, generation, reasoning, research, structured_outputs, toolschat, generation, reasoning, tools

Muse Spark 1.2 Capabilities

chatcomputer-usegenerationreasoningresearchstructured outputstools
Serving providers0
Canonical IDmeta-llama/muse-spark-1.2

GLM 5.1 Capabilities

chatgenerationreasoningtools
Serving providers4
Canonical IDzai-org/GLM-5.1

Primary Evidence

Sources and Freshness

Questions

Muse Spark 1.2 vs GLM 5.1 FAQs

Is Muse Spark 1.2 or GLM 5.1 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Muse Spark 1.2 and GLM 5.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Muse Spark 1.2 or GLM 5.1?+

Only GLM 5.1 has a directly sourced input price: $1.05 per million tokens. Only GLM 5.1 has a directly sourced output price: $3.50 per million tokens.

Which has a larger context window, Muse Spark 1.2 or GLM 5.1?+

Neither model has a larger sourced context window in this comparison. Muse Spark 1.2 is — and GLM 5.1 is 203K.

Which performs better in benchmarks, Muse Spark 1.2 or GLM 5.1?+

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

Can Muse Spark 1.2 or GLM 5.1 be self-hosted?+

GLM 5.1 is the only model in this pair currently marked as self-hostable. Muse Spark 1.2 is not marked open weight; GLM 5.1 is open weight.

Can Muse Spark 1.2 and GLM 5.1 understand images?+

Muse Spark 1.2 is documented with image input; GLM 5.1 is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Muse Spark 1.2 or GLM 5.1?+

Neither has a larger sourced maximum output. Muse Spark 1.2 is — and GLM 5.1 is —.

Do Muse Spark 1.2 and GLM 5.1 support reasoning and tool use?+

Muse Spark 1.2: reasoning, tool calling, and image input. GLM 5.1: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, Muse Spark 1.2 or GLM 5.1?+

Muse Spark 1.2 has 0 sourced provider routes; GLM 5.1 has 4, so GLM 5.1 has broader tracked availability.

Which offers better value, Muse Spark 1.2 or GLM 5.1?+

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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