Qwen3.7 Max vs GLM 5V Turbo

At a Glance

Compare
Intelligence, Cost, and Efficiency
CostLower is better · Published-token output estimate#13 of 44$0.057 per LiveBench caseUnrankedNot in the 44-model eligible cohort
Pricing and Limits
Input priceFrom · USD / 1M tokens$1.475Openrouter · Sep 22, 2026Not reported
Output priceFrom · USD / 1M tokens$4.425Openrouter · Sep 22, 2026Not reported
Context windowMaximum documented tokens1,000K200K
Model facts checkedSep 3, 2026View model evidence →Aug 29, 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 →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldQwen3.7 MaxGLM-5V-Turbo
DeveloperQwenZ.ai
FamilyQwen3 7Glm 5v
ModelQwen3.7 MaxGLM-5V-Turbo
VersionQwen3.7 MaxGLM-5V-Turbo
Lifecycleactiveactive
Released2026-05-20Unknown
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, VideoText, Image, Video, Document
Output modalitiesTextText
Context window1,000K200K
Total parametersUnknownUnknown
Active parametersUnknownUnknown
LicenseUnknownUnknown
Open weightsNoNo
API availableYesYes
Self-hostableNoNo
Provider accessAlibaba Cloud Model Studio (Standard), Deepinfra (Standard), Openrouter (Standard), Together Ai (Standard)Z.ai (Standard)
Capabilitiesagents, chat, generation, reasoning, structured_outputs, tools, visionagents, chat, computer-use, reasoning, tools, vision

Qwen3.7 Max Capabilities

agentschatgenerationreasoningstructured outputstoolsvision
Serving providers4
Canonical IDqwen/qwen3.7-max

GLM 5V Turbo Capabilities

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

Primary Evidence

Sources and Freshness

Questions

Qwen3.7 Max vs GLM 5V Turbo FAQs

Is Qwen3.7 Max or GLM 5V Turbo better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.7 Max and GLM 5V Turbo, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Qwen3.7 Max or GLM 5V Turbo?+

Only Qwen3.7 Max has a directly sourced input price: $1.475 per million tokens. Only Qwen3.7 Max has a directly sourced output price: $4.425 per million tokens.

Which has a larger context window, Qwen3.7 Max or GLM 5V Turbo?+

Qwen3.7 Max has the larger sourced context window. Qwen3.7 Max supports 1,000K and GLM 5V Turbo supports 200K.

Which performs better in benchmarks, Qwen3.7 Max 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 Qwen3.7 Max or GLM 5V Turbo be self-hosted?+

Both models have the same recorded self-hosting status: unsupported. Qwen3.7 Max is not marked open weight; GLM 5V Turbo is not marked open weight.

Can Qwen3.7 Max and GLM 5V Turbo understand images?+

Qwen3.7 Max 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, Qwen3.7 Max or GLM 5V Turbo?+

GLM 5V Turbo has the larger sourced maximum output: Qwen3.7 Max supports 66K and GLM 5V Turbo supports 131K output tokens.

Do Qwen3.7 Max and GLM 5V Turbo support reasoning and tool use?+

Qwen3.7 Max: 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, Qwen3.7 Max or GLM 5V Turbo?+

Qwen3.7 Max has 4 sourced provider routes; GLM 5V Turbo has 1, so Qwen3.7 Max has broader tracked availability.

Which offers better value, Qwen3.7 Max 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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