Qwen3.8 Max vs GLM 5.3 Flash

At a Glance

Compare
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#10 of 4679.2 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 52.8–86.1#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#23 of 44$0.107 per LiveBench case#2 of 44$0.0087 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5#6 of 3863.2 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 50.0–66.7#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$1.65Deepinfra · Sep 22, 2026$0.075Z.ai · Aug 29, 2026
Output priceFrom · USD / 1M tokens$4.951Deepinfra · Sep 22, 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 →
BenchmarkQwen3.8-MaxGLM-5.3-Flash
LMArena Agent Arenaagent-2026-09-15-d25aabda0010 · outcome_score · leader3.30100% of row best · score · Qwen3.8 Max; 95% CI [2.46604139, 4.14181105]; sessions 31489; observations 2502655; rank 171.1598% 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 · statistical tie1,480.55100% of row best · rating · qwen3.8-max; 95% CI [1474.79040793, 1486.31846927]; votes 16670; rank 141,471.8999% 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 tie1,315.33100% of row best · rating · qwen3.8-max; 95% CI [1307.40892164, 1323.25970514]; votes 8665; rank 61,298.7199% of row best · rating · glm-5.3-flash; 95% CI [1287.06707337, 1310.34654848]; votes 3110; rank 17
LiveBench2026-06-25 · overall · leader81.88100% of row best · percent · qwen3.8-max · 21,637 output tokens / case73.2789% of row best · percent · glm-5.3-flash · 34,707 output tokens / case
ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified83.3294% of row best · points · Qwen3.8 Max · 22,888 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 · 2 ties2 benchmark winsOverall lead0 benchmark wins

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

FieldQwen3.8-MaxGLM-5.3-Flash
DeveloperQwenZ.ai
FamilyQwen3 8 MaxGlm 5 3 Flash
ModelQwen3.8-MaxGLM-5.3-Flash
VersionQwen3.8-MaxGLM-5.3-Flash
Lifecycleactiveactive
ReleasedUnknown2026-09-02
Knowledge cutoffUnknownUnknown
Input modalitiesText, Image, VideoText, Image, Video, Document
Output modalitiesTextText
Context window1,000K1,000K
Total parameters2.4T320B
Active parametersUnknown18B
LicenseUnknownMIT
Open weightsNoYes
API availableYesYes
Self-hostableNoYes
Provider accessAlibaba Cloud Model Studio (Standard), Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard)Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard)
Capabilitiesagents, chat, reasoning, structured_outputs, tools, visionagents, chat, computer-use, reasoning, structured_outputs, tools, vision

Qwen3.8 Max Capabilities

agentschatreasoningstructured outputstoolsvision
Serving providers4
Canonical IDqwen/qwen3.8-max

GLM 5.3 Flash Capabilities

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

Primary Evidence

Sources and Freshness

Questions

Qwen3.8 Max vs GLM 5.3 Flash FAQs

Is Qwen3.8 Max or GLM 5.3 Flash better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.8 Max and GLM 5.3 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Qwen3.8 Max or GLM 5.3 Flash?+

Qwen3.8 Max is $1.65 and GLM 5.3 Flash is $0.075 per million tokens, so GLM 5.3 Flash is cheaper on this metric. Qwen3.8 Max is $4.951 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, Qwen3.8 Max or GLM 5.3 Flash?+

Neither model has a larger sourced context window in this comparison. Qwen3.8 Max is 1,000K and GLM 5.3 Flash is 1,000K.

Which performs better in benchmarks, Qwen3.8 Max or GLM 5.3 Flash?+

Qwen3.8 Max leads the current overall benchmark count. The result uses 4 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.

Can Qwen3.8 Max or GLM 5.3 Flash be self-hosted?+

GLM 5.3 Flash is the only model in this pair currently marked as self-hostable. Qwen3.8 Max is not marked open weight; GLM 5.3 Flash is open weight.

Can Qwen3.8 Max and GLM 5.3 Flash understand images?+

Qwen3.8 Max 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, Qwen3.8 Max or GLM 5.3 Flash?+

Neither has a larger sourced maximum output. Qwen3.8 Max is 131K and GLM 5.3 Flash is 131K.

Do Qwen3.8 Max and GLM 5.3 Flash support reasoning and tool use?+

Qwen3.8 Max: 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, Qwen3.8 Max or GLM 5.3 Flash?+

Qwen3.8 Max has 4 sourced provider routes; GLM 5.3 Flash has 4, a tie.

Which offers better value, Qwen3.8 Max 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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