Gemini 3.7 Flash vs GLM-5V-Turbo

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
Google DeepMind · activeGemini 3.7 FlashVerified Aug 29, 2026
Z.ai · activeGLM-5V-TurboVerified Aug 29, 2026

Technical Differences

Side-by-Side Facts

Indexable
FieldGemini 3.7 FlashGLM-5V-Turbo
DeveloperGoogle DeepMindZ.ai
FamilyGemini 3Glm 5v
ModelGemini 3.7 FlashGLM-5V-Turbo
VersionGemini 3.7 FlashGLM-5V-Turbo
Lifecycleactiveactive
ReleasedUnknownUnknown
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, Video, Audio, DocumentText, Image, Video, Document
Output modalitiesTextText
Context window1,048,576200,000
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessGoogle AI (Standard)Z.ai (Standard)
Capabilitieschat, generation, reasoning, toolsagents, chat, computer-use, reasoning, tools, vision

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

Gemini 3.7 Flash Capabilities

chatgenerationreasoningtools
Input price$0.75
Output price$3.75
Serving providers1
Canonical IDgoogle-deepmind/gemini-3.7-flash

GLM-5V-Turbo Capabilities

agentschatcomputer-usereasoningtoolsvision
Input price
Output price
Serving providers1
Canonical IDzai-org/glm-5v-turbo

Internal Comparison Graph

Related Comparisons

All text comparisons →
APairBContext
vsfamily variantsimage, text, video
vscross-developer peersimage, text, video
vscross-developer peersimage, text, video
vscross-developer peersimage, text, video
vsfamily variantstext
vscross-developer peersimage, text
vscross-developer peersimage, text
vsfamily variantsimage, text
vscross-developer peerstext
vscross-developer peerstext
vscross-developer peerstext
vscross-developer peerstext

Primary Evidence

Sources and Freshness

Questions

Gemini 3.7 Flash vs GLM-5V-Turbo FAQs

Is Gemini 3.7 Flash or GLM-5V-Turbo better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Gemini 3.7 Flash and GLM-5V-Turbo, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Gemini 3.7 Flash or GLM-5V-Turbo?+

Only Gemini 3.7 Flash has a directly sourced input price: $0.75 per million tokens. Only Gemini 3.7 Flash has a directly sourced output price: $3.75 per million tokens.

Which has a larger context window, Gemini 3.7 Flash or GLM-5V-Turbo?+

Gemini 3.7 Flash has the larger sourced context window. Gemini 3.7 Flash supports 1,048,576 and GLM-5V-Turbo supports 200,000.

Which performs better in benchmarks, Gemini 3.7 Flash or GLM-5V-Turbo?+

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

Can Gemini 3.7 Flash or GLM-5V-Turbo be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. Gemini 3.7 Flash is not marked open weight; GLM-5V-Turbo is not marked open weight.

Can Gemini 3.7 Flash and GLM-5V-Turbo understand images?+

Gemini 3.7 Flash is documented with image input; GLM-5V-Turbo is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Gemini 3.7 Flash or GLM-5V-Turbo?+

GLM-5V-Turbo has the larger sourced maximum output: Gemini 3.7 Flash supports 65,536 and GLM-5V-Turbo supports 131,072 output tokens.

Do Gemini 3.7 Flash and GLM-5V-Turbo support reasoning and tool use?+

Gemini 3.7 Flash: reasoning, tool calling, and image input. GLM-5V-Turbo: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Gemini 3.7 Flash or GLM-5V-Turbo?+

Gemini 3.7 Flash has 1 sourced provider route; GLM-5V-Turbo has 1, a tie.

Which offers better value, Gemini 3.7 Flash or GLM-5V-Turbo?+

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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