Qwen3.8 27B vs Grok Build 0.1

At a Glance

Compare
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#30 of 4654.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.0–69.4UnrankedNot in the 46-model eligible cohort
CostLower is better · Published-token output estimate#16 of 44$0.072 per LiveBench case#1 of 44$0.0020 per LiveBench case
EfficiencyHigher is better · MM Efficiency v1.5#17 of 3854.8 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 45.8–62.5UnrankedNot in the 38-model eligible cohort
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.20Deepinfra · Sep 22, 2026$1.00Xai · Sep 3, 2026
Output priceFrom · USD / 1M tokens$2.50Deepinfra · Sep 22, 2026$2.00Xai · Sep 3, 2026
Context windowMaximum documented tokens262K256K
Model facts checkedAug 28, 2026View model evidence →Sep 3, 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 →
BenchmarkQwen3.8-27BGrok Build 0.1
LiveBench2026-06-25 · overall · leader78.02100% of row best · percent · qwen3.8-27b · 28,740 output tokens / case71.0991% of row best · percent · grok-build-0.1 · 980 output tokens / case
Overall ResultCounted from the protocol-matched rows above1 benchmark winNo overall winner0 benchmark winsNo overall winner

Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.

Quality Versus Estimated Output Cost

Full ranking →
Efficiency FrontierLiveBench overall · output estimate
Upper-left is better
xAIZ.aiMiniMaxDeepSeekOpenAIQwenGoogle DeepMindMoonshot AIAnthropic
LiveBench quality versus score-adjusted output costEach dot is a reviewed major-model configuration and is colored by developer. Higher means a better LiveBench overall score. Farther left means lower estimated output cost after adjusting by the score. A dotted line connects the non-dominated frontier observations. When models are selected, their sourced families remain prominent, unrelated observations retain their developer colors at lower opacity, and an orange ring identifies each selected model.$0.0050$0.010$0.050$0.100$0.500$1.006873798489Grok Build 0.1GLM 5.3 FlashDeepSeek V4.1 Flash (max)GPT-5.6 Sol (max)GPT-6 Astra (max)Claude Fable 5.1Score-adjusted output cost per LiveBench case (log) →LiveBench overall →
The dotted frontier connects measured, non-dominated major-model observations. With a selection, sourced families stay prominent, unrelated observations retain their developer colors at lower opacity, and orange rings mark the selected model or models. Family lines connect models only when their sourced family and generation match. Cost is estimated from published output tokens and the lowest current USD output rate; it excludes input, caching, batch discounts, and provider-specific benchmark execution details.

Side-by-Side Facts

FieldQwen3.8-27BGrok Build 0.1
DeveloperQwenxAI
FamilyQwen3 8 27bGrok Build
ModelQwen3.8-27BGrok Build 0.1
VersionQwen3.8-27BGrok Build 0.1
Lifecycleactivepreview
ReleasedUnknown2026-05-29
Knowledge cutoffUnknownUnknown
Input modalitiesText, ImageText, Image
Output modalitiesTextText
Context window262K256K
Total parameters27.8BUnknown
Active parametersUnknownUnknown
Licenseapache-2.0Unknown
Open weightsYesNo
API availableYesYes
Self-hostableYesNo
Provider accessDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard)Xai (Standard)
Capabilitieschat, generation, reasoning, toolschat, generation, reasoning, structured_outputs, tools

Qwen3.8 27B Capabilities

chatgenerationreasoningtools
Serving providers3
Canonical IDQwen/Qwen3.8-27B

Grok Build 0.1 Capabilities

chatgenerationreasoningstructured outputstools
Serving providers1
Canonical IDxai/grok-build-0.1

Primary Evidence

Sources and Freshness

Questions

Qwen3.8 27B vs Grok Build 0.1 FAQs

Is Qwen3.8 27B or Grok Build 0.1 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.8 27B and Grok Build 0.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Qwen3.8 27B or Grok Build 0.1?+

Qwen3.8 27B is $0.20 and Grok Build 0.1 is $1.00 per million tokens, so Qwen3.8 27B is cheaper on this metric. Qwen3.8 27B is $2.50 and Grok Build 0.1 is $2.00 per million tokens, so Grok Build 0.1 is cheaper on this metric.

Which has a larger context window, Qwen3.8 27B or Grok Build 0.1?+

Qwen3.8 27B has the larger sourced context window. Qwen3.8 27B supports 262K and Grok Build 0.1 supports 256K.

Which performs better in benchmarks, Qwen3.8 27B or Grok Build 0.1?+

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

Can Qwen3.8 27B or Grok Build 0.1 be self-hosted?+

Qwen3.8 27B is the only model in this pair currently marked as self-hostable. Qwen3.8 27B is open weight; Grok Build 0.1 is not marked open weight.

Can Qwen3.8 27B and Grok Build 0.1 understand images?+

Qwen3.8 27B is documented with image input; Grok Build 0.1 is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Qwen3.8 27B or Grok Build 0.1?+

Neither has a larger sourced maximum output. Qwen3.8 27B is 131K and Grok Build 0.1 is —.

Do Qwen3.8 27B and Grok Build 0.1 support reasoning and tool use?+

Qwen3.8 27B: reasoning, tool calling, and image input. Grok Build 0.1: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Qwen3.8 27B or Grok Build 0.1?+

Qwen3.8 27B has 3 sourced provider routes; Grok Build 0.1 has 1, so Qwen3.8 27B has broader tracked availability.

Which offers better value, Qwen3.8 27B or Grok Build 0.1?+

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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