Claude Opus 4.8 vs GLM 5.3 Flash

At a Glance

Compare
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#18 of 4670.1 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#42 of 44$0.604 per LiveBench case#2 of 44$0.0087 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5#35 of 3840.5 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$5.00Anthropic · Sep 3, 2026$0.075Z.ai · Aug 29, 2026
Output priceFrom · USD / 1M tokens$25.00Anthropic · Sep 3, 2026$0.25Z.ai · Aug 29, 2026
Context windowMaximum documented tokens1,000K1,000K
Model facts checkedAug 29, 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 →
BenchmarkClaude Opus 4.8GLM-5.3-Flash
LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader8.19100% of row best · score · Claude Opus 4.8 (High); 95% CI [6.91963673, 9.45737628]; sessions 39731; observations 2097389; rank 61.1593% 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 · leader1,452.6699% of row best · rating · claude-opus-4-8; 95% CI [1448.52490690, 1456.79817098]; votes 53446; rank 401,471.89100% 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 · statistical tie1,288.2399% of row best · rating · claude-opus-4-8; 95% CI [1281.27991800, 1295.18871241]; votes 15801; rank 251,298.71100% of row best · rating · glm-5.3-flash; 95% CI [1287.06707337, 1310.34654848]; votes 3110; rank 17
LiveBench2026-06-25 · overall · leader81.18100% of row best · percent · claude-opus-4-8-max-effort · 24,171 output tokens / case73.2790% of row best · percent · glm-5.3-flash · 34,707 output tokens / case
ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified87.91100% of row best · points · Claude Opus 4.8 (max effort) · 24,846 output tokens / case88.19100% of row best · points · GLM-5.3 Flash · 25,960 output tokens / case
Overall ResultCounted from the protocol-matched rows above · 1 tie2 benchmark winsOverall lead1 benchmark win

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

FieldClaude Opus 4.8GLM-5.3-Flash
DeveloperAnthropicZ.ai
FamilyClaude 4 8Glm 5 3 Flash
ModelClaude Opus 4.8GLM-5.3-Flash
VersionClaude Opus 4.8GLM-5.3-Flash
Lifecycleactiveactive
Released2026-05-282026-09-02
Knowledge cutoff2026-01-01Unknown
Input modalitiesText, ImageText, Image, Video, Document
Output modalitiesTextText
Context window1,000K1,000K
Total parametersUnknown320B
Active parametersUnknown18B
LicenseUnknownMIT
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessAnthropic (Standard), Deepinfra (Standard)Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard)
Capabilitieschat, generation, reasoning, toolsagents, chat, computer-use, reasoning, structured_outputs, tools, vision

Claude Opus 4.8 Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDanthropic/claude-opus-4-8

GLM 5.3 Flash Capabilities

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

Primary Evidence

Sources and Freshness

Questions

Claude Opus 4.8 vs GLM 5.3 Flash FAQs

Is Claude Opus 4.8 or GLM 5.3 Flash better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Claude Opus 4.8 and GLM 5.3 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Claude Opus 4.8 or GLM 5.3 Flash?+

Claude Opus 4.8 is $5.00 and GLM 5.3 Flash is $0.075 per million tokens, so GLM 5.3 Flash is cheaper on this metric. Claude Opus 4.8 is $25.00 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, Claude Opus 4.8 or GLM 5.3 Flash?+

Neither model has a larger sourced context window in this comparison. Claude Opus 4.8 is 1,000K and GLM 5.3 Flash is 1,000K.

Which performs better in benchmarks, Claude Opus 4.8 or GLM 5.3 Flash?+

Claude Opus 4.8 leads the current overall benchmark count. The result uses 4 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.

Can Claude Opus 4.8 or GLM 5.3 Flash be self-hosted?+

GLM 5.3 Flash is the only model in this pair currently marked as self-hostable. Claude Opus 4.8 is not marked open weight; GLM 5.3 Flash is open weight.

Can Claude Opus 4.8 and GLM 5.3 Flash understand images?+

Claude Opus 4.8 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, Claude Opus 4.8 or GLM 5.3 Flash?+

GLM 5.3 Flash has the larger sourced maximum output: Claude Opus 4.8 supports 128K and GLM 5.3 Flash supports 131K output tokens.

Do Claude Opus 4.8 and GLM 5.3 Flash support reasoning and tool use?+

Claude Opus 4.8: 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, Claude Opus 4.8 or GLM 5.3 Flash?+

Claude Opus 4.8 has 2 sourced provider routes; GLM 5.3 Flash has 4, so GLM 5.3 Flash has broader tracked availability.

Which offers better value, Claude Opus 4.8 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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