DeepSeek V4 Flash Base vs GLM 5.3

Model Markets Rankings

Intelligence, Cost, and Efficiency

All rankings →
RankingDeepSeek-V4-Flash-BaseGLM-5.3
CostLower is better · Published-token output estimateUnrankedNot in the 36-model eligible cohort#28 of 36$0.248 per LiveBench case

Ranks come from the current complete eligible cohorts. Green highlights appear only when both models are ranked in the same metric. Missing required inputs remain unranked, and the three dimensions are not collapsed into an overall winner.

Benchmark Performance

Available Benchmarks

No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.
FieldAt a Glance
DeepSeek · activeDeepSeek V4 Flash BaseVerified Aug 28, 2026
Z.ai · activeGLM 5.3Verified Aug 29, 2026

Technical Differences

Side-by-Side Facts

Indexable
FieldDeepSeek-V4-Flash-BaseGLM-5.3
DeveloperDeepSeekZ.ai
FamilyDeepseek V4 Flash BaseGlm 5 3
ModelDeepSeek-V4-Flash-BaseGLM-5.3
VersionDeepSeek-V4-Flash-BaseGLM-5.3
Lifecycleactiveactive
Released2026-04-24Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesTextText
Context window1,049K1,000K
Total parameters292BUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsYesNo
API availableUnknownYes
Self-hostableYesNo
Provider accessUnknownDeepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard)
Capabilitiesgenerationagents, chat, reasoning, structured_outputs, tools

11 comparable fields · 8 material differences · Pair passes the primary-source comparison gate

DeepSeek V4 Flash Base Capabilities

generation
Input price
Output price
Serving providers0
Canonical IDdeepseek-ai/DeepSeek-V4-Flash-Base

GLM 5.3 Capabilities

agentschatreasoningstructured outputstools
Input price$1.20
Output price$4.00
Serving providers4
Canonical IDzai-org/glm-5.3

Internal Comparison Graph

Related Comparisons

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Primary Evidence

Sources and Freshness

Questions

DeepSeek V4 Flash Base vs GLM 5.3 FAQs

Is DeepSeek V4 Flash Base or GLM 5.3 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Flash Base and GLM 5.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, DeepSeek V4 Flash Base or GLM 5.3?+

Only GLM 5.3 has a directly sourced input price: $1.20 per million tokens. Only GLM 5.3 has a directly sourced output price: $4.00 per million tokens.

Which has a larger context window, DeepSeek V4 Flash Base or GLM 5.3?+

DeepSeek V4 Flash Base has the larger sourced context window. DeepSeek V4 Flash Base supports 1,049K and GLM 5.3 supports 1,000K.

Which performs better in benchmarks, DeepSeek V4 Flash Base or GLM 5.3?+

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

Can DeepSeek V4 Flash Base or GLM 5.3 be self-hosted?+

DeepSeek V4 Flash Base is the only model in this pair currently marked as self-hostable. DeepSeek V4 Flash Base is open weight; GLM 5.3 is not marked open weight.

Can DeepSeek V4 Flash Base and GLM 5.3 understand images?+

DeepSeek V4 Flash Base is not documented with image input; GLM 5.3 is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, DeepSeek V4 Flash Base or GLM 5.3?+

Neither has a larger sourced maximum output. DeepSeek V4 Flash Base is — and GLM 5.3 is 131K.

Do DeepSeek V4 Flash Base and GLM 5.3 support reasoning and tool use?+

DeepSeek V4 Flash Base: none of these features are definitively sourced. GLM 5.3: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, DeepSeek V4 Flash Base or GLM 5.3?+

DeepSeek V4 Flash Base has 0 sourced provider routes; GLM 5.3 has 4, so GLM 5.3 has broader tracked availability.

Which offers better value, DeepSeek V4 Flash Base or GLM 5.3?+

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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