Devstral 2 123B Instruct 2512 vs GLM 5

At a Glance

Compare
GLM 5Z.ai
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.44Openrouter · Aug 29, 2026$0.60Deepinfra · Sep 3, 2026
Output priceFrom · USD / 1M tokens$2.20Openrouter · Aug 29, 2026$1.92Openrouter · Aug 28, 2026
Context windowMaximum documented tokens262K203K
Model facts checkedAug 28, 2026View model evidence →Aug 28, 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

FieldDevstral-2-123B-Instruct-2512GLM-5
DeveloperMistral AIZ.ai
FamilyDevstral 2 123b Instruct 2512Glm 5
ModelDevstral-2-123B-Instruct-2512GLM-5
VersionDevstral-2-123B-Instruct-2512GLM-5
Lifecycleactiveactive
ReleasedUnknownUnknown
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesTextText
Context window262K203K
Total parameters125B753.9B
Active parametersUnknownUnknown
Licenseothermit
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessOpenrouter (Standard)Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard)
Capabilitieschat, generation, toolschat, generation, reasoning, tools

Devstral 2 123B Instruct 2512 Capabilities

chatgenerationtools
Serving providers1
Canonical IDmistralai/Devstral-2-123B-Instruct-2512

GLM 5 Capabilities

chatgenerationreasoningtools
Serving providers4
Canonical IDzai-org/GLM-5

Primary Evidence

Sources and Freshness

Questions

Devstral 2 123B Instruct 2512 vs GLM 5 FAQs

Is Devstral 2 123B Instruct 2512 or GLM 5 better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Devstral 2 123B Instruct 2512 and GLM 5, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Devstral 2 123B Instruct 2512 or GLM 5?+

Devstral 2 123B Instruct 2512 is $0.44 and GLM 5 is $0.60 per million tokens, so Devstral 2 123B Instruct 2512 is cheaper on this metric. Devstral 2 123B Instruct 2512 is $2.20 and GLM 5 is $1.92 per million tokens, so GLM 5 is cheaper on this metric.

Which has a larger context window, Devstral 2 123B Instruct 2512 or GLM 5?+

Devstral 2 123B Instruct 2512 has the larger sourced context window. Devstral 2 123B Instruct 2512 supports 262K and GLM 5 supports 203K.

Which performs better in benchmarks, Devstral 2 123B Instruct 2512 or GLM 5?+

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

Can Devstral 2 123B Instruct 2512 or GLM 5 be self-hosted?+

Both models have the same recorded self-hosting status: supported. Devstral 2 123B Instruct 2512 is open weight; GLM 5 is open weight.

Can Devstral 2 123B Instruct 2512 and GLM 5 understand images?+

Devstral 2 123B Instruct 2512 is not documented with image input; GLM 5 is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Devstral 2 123B Instruct 2512 or GLM 5?+

Neither has a larger sourced maximum output. Devstral 2 123B Instruct 2512 is — and GLM 5 is —.

Do Devstral 2 123B Instruct 2512 and GLM 5 support reasoning and tool use?+

Devstral 2 123B Instruct 2512: tool calling. GLM 5: reasoning and tool calling. Feature support does not establish relative quality.

Which is available from more inference providers, Devstral 2 123B Instruct 2512 or GLM 5?+

Devstral 2 123B Instruct 2512 has 1 sourced provider route; GLM 5 has 4, so GLM 5 has broader tracked availability.

Which offers better value, Devstral 2 123B Instruct 2512 or GLM 5?+

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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