GPT-5.6 Luna vs GLM 5.3 Flash

At a Glance

Compare
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#31 of 4652.7 score · 3/3 sources · complete#29 of 4654.6 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.4–69.8
CostLower is better · Published-token output estimate#6 of 44$0.026 per LiveBench case#2 of 44$0.0087 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5#4 of 3864.8 score · 3/3 sources · complete#1 of 3877.3 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 68.2–84.9
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.20Openai · Sep 3, 2026$0.075Z.ai · Aug 29, 2026
Output priceFrom · USD / 1M tokens$1.20Openai · Sep 3, 2026$0.25Z.ai · Aug 29, 2026
Context windowMaximum documented tokens1,050K1,000K
Model facts checkedAug 28, 2026View model evidence →Sep 2, 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 →
BenchmarkGPT-5.6 LunaGLM-5.3-Flash
LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader-0.4498% of row best · score · GPT 5.6 Luna (xHigh); 95% CI [-1.27310649, 0.39719069]; sessions 29186; observations 2307801; rank 271.15100% of row best · score · GLM 5.3 Flash; 95% CI [0.48275443, 1.81278415]; sessions 43164; observations 4433017; rank 25
LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader1,429.8997% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1425.11549148, 1434.66063238]; votes 28547; rank 861,471.89100% of row best · rating · glm-5.3-flash; 95% CI [1465.37026588, 1478.41920488]; votes 10038; rank 24
LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader1,258.5397% of row best · rating · gpt-5.6-luna-xhigh; 95% CI [1250.17799393, 1266.87249678]; votes 7793; rank 481,298.71100% of row best · rating · glm-5.3-flash; 95% CI [1287.06707337, 1310.34654848]; votes 3110; rank 17
LiveBench2026-06-25 · overall · leader77.05100% of row best · percent · gpt-5.6-luna-max · 21,799 output tokens / case73.2795% of row best · percent · glm-5.3-flash · 34,707 output tokens / case
ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified84.4796% of row best · points · GPT-5.6 Luna (ultra) · 17,605 output tokens / case88.19100% of row best · points · GLM-5.3 Flash · 25,960 output tokens / case
Overall ResultCounted from the protocol-matched rows above1 benchmark win3 benchmark winsOverall lead

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

Quality Versus Estimated Output Cost

Full ranking →
Efficiency FrontierLiveBench overall · output estimate
Upper-left is better
xAIZ.aiMiniMaxDeepSeekOpenAIQwenGoogle DeepMindMoonshot AIAnthropic
LiveBench quality versus score-adjusted output costEach dot is a reviewed major-model configuration and is colored by developer. Higher means a better LiveBench overall score. Farther left means lower estimated output cost after adjusting by the score. A dotted line connects the non-dominated frontier observations. When models are selected, their sourced families remain prominent, unrelated observations retain their developer colors at lower opacity, and an orange ring identifies each selected model.$0.0050$0.010$0.050$0.100$0.500$1.006873798489Grok Build 0.1GLM 5.3 FlashDeepSeek V4.1 Flash (max)GPT-5.6 Sol (max)GPT-6 Astra (max)Claude Fable 5.1Score-adjusted output cost per LiveBench case (log) →LiveBench overall →
The dotted frontier connects measured, non-dominated major-model observations. With a selection, sourced families stay prominent, unrelated observations retain their developer colors at lower opacity, and orange rings mark the selected model or models. Family lines connect models only when their sourced family and generation match. Cost is estimated from published output tokens and the lowest current USD output rate; it excludes input, caching, batch discounts, and provider-specific benchmark execution details.

Side-by-Side Facts

FieldGPT-5.6 LunaGLM-5.3-Flash
DeveloperOpenAIZ.ai
FamilyGpt 5 6Glm 5 3 Flash
ModelGPT-5.6 LunaGLM-5.3-Flash
VersionGPT-5.6 LunaGLM-5.3-Flash
Lifecycleactiveactive
ReleasedUnknown2026-09-02
Knowledge cutoff2026-02-16Unknown
Input modalitiesText, ImageText, Image, Video, Document
Output modalitiesTextText
Context window1,050K1,000K
Total parametersUnknown320B
Active parametersUnknown18B
LicenseUnknownMIT
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessOpenai (Standard), Openrouter (Standard)Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard)
Capabilitieschat, generation, reasoning, toolsagents, chat, computer-use, reasoning, structured_outputs, tools, vision

GPT-5.6 Luna Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDopenai/gpt-5.6-luna

GLM 5.3 Flash Capabilities

agentschatcomputer-usereasoningstructured outputstoolsvision
Serving providers4
Canonical IDzai-org/glm-5.3-flash

Primary Evidence

Sources and Freshness

Questions

GPT-5.6 Luna vs GLM 5.3 Flash FAQs

Is GPT-5.6 Luna or GLM 5.3 Flash better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both GPT-5.6 Luna and GLM 5.3 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, GPT-5.6 Luna or GLM 5.3 Flash?+

GPT-5.6 Luna is $0.20 and GLM 5.3 Flash is $0.075 per million tokens, so GLM 5.3 Flash is cheaper on this metric. GPT-5.6 Luna is $1.20 and GLM 5.3 Flash is $0.25 per million tokens, so GLM 5.3 Flash is cheaper on this metric.

Which has a larger context window, GPT-5.6 Luna or GLM 5.3 Flash?+

GPT-5.6 Luna has the larger sourced context window. GPT-5.6 Luna supports 1,050K and GLM 5.3 Flash supports 1,000K.

Which performs better in benchmarks, GPT-5.6 Luna or GLM 5.3 Flash?+

GLM 5.3 Flash leads the current overall benchmark count. The result uses 4 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.

Can GPT-5.6 Luna or GLM 5.3 Flash be self-hosted?+

GLM 5.3 Flash is the only model in this pair currently marked as self-hostable. GPT-5.6 Luna is not marked open weight; GLM 5.3 Flash is open weight.

Can GPT-5.6 Luna and GLM 5.3 Flash understand images?+

GPT-5.6 Luna is documented with image input; GLM 5.3 Flash is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, GPT-5.6 Luna or GLM 5.3 Flash?+

GLM 5.3 Flash has the larger sourced maximum output: GPT-5.6 Luna supports 128K and GLM 5.3 Flash supports 131K output tokens.

Do GPT-5.6 Luna and GLM 5.3 Flash support reasoning and tool use?+

GPT-5.6 Luna: reasoning, tool calling, and image input. GLM 5.3 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, GPT-5.6 Luna or GLM 5.3 Flash?+

GPT-5.6 Luna has 2 sourced provider routes; GLM 5.3 Flash has 4, so GLM 5.3 Flash has broader tracked availability.

Which offers better value, GPT-5.6 Luna or GLM 5.3 Flash?+

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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