Olmo 3.1 32B Instruct vs Sonar Deep Research

At a Glance

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Pricing and Limits
Input priceFrom · USD / 1M tokensNot reported$2.00Openrouter · Sep 22, 2026
Output priceFrom · USD / 1M tokensNot reported$8.00Openrouter · Sep 22, 2026
Context windowMaximum documented tokens66K128K
Model facts checkedAug 28, 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

FieldOlmo-3.1-32B-InstructSonar Deep Research
DeveloperAi2Perplexity
FamilyOlmo 3 1 32b InstructSonar
ModelOlmo-3.1-32B-InstructSonar Deep Research
VersionOlmo-3.1-32B-InstructSonar Deep Research
Lifecycleactiveactive
ReleasedUnknownUnknown
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesTextText
Context window66K128K
Total parameters32.2BUnknown
Active parametersUnknownUnknown
Licenseapache-2.0Unknown
Open weightsYesNo
API availableUnknownYes
Self-hostableYesNo
Provider accessUnknownOpenrouter (Standard), Perplexity (Standard)
Capabilitieschat, generation, toolschat, citations, reasoning, research, search

Olmo 3.1 32B Instruct Capabilities

chatgenerationtools
Serving providers0
Canonical IDallenai/Olmo-3.1-32B-Instruct

Sonar Deep Research Capabilities

chatcitationsreasoningresearchsearch
Serving providers2
Canonical IDperplexity/sonar-deep-research

Primary Evidence

Sources and Freshness

Questions

Olmo 3.1 32B Instruct vs Sonar Deep Research FAQs

Is Olmo 3.1 32B Instruct or Sonar Deep Research better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Olmo 3.1 32B Instruct and Sonar Deep Research, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Olmo 3.1 32B Instruct or Sonar Deep Research?+

Only Sonar Deep Research has a directly sourced input price: $2.00 per million tokens. Only Sonar Deep Research has a directly sourced output price: $8.00 per million tokens.

Which has a larger context window, Olmo 3.1 32B Instruct or Sonar Deep Research?+

Sonar Deep Research has the larger sourced context window. Olmo 3.1 32B Instruct supports 66K and Sonar Deep Research supports 128K.

Which performs better in benchmarks, Olmo 3.1 32B Instruct or Sonar Deep Research?+

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

Can Olmo 3.1 32B Instruct or Sonar Deep Research be self-hosted?+

Olmo 3.1 32B Instruct is the only model in this pair currently marked as self-hostable. Olmo 3.1 32B Instruct is open weight; Sonar Deep Research is not marked open weight.

Can Olmo 3.1 32B Instruct and Sonar Deep Research understand images?+

Olmo 3.1 32B Instruct is not documented with image input; Sonar Deep Research is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Olmo 3.1 32B Instruct or Sonar Deep Research?+

Neither has a larger sourced maximum output. Olmo 3.1 32B Instruct is 33K and Sonar Deep Research is —.

Do Olmo 3.1 32B Instruct and Sonar Deep Research support reasoning and tool use?+

Olmo 3.1 32B Instruct: tool calling. Sonar Deep Research: reasoning. Feature support does not establish relative quality.

Which is available from more inference providers, Olmo 3.1 32B Instruct or Sonar Deep Research?+

Olmo 3.1 32B Instruct has 0 sourced provider routes; Sonar Deep Research has 2, so Sonar Deep Research has broader tracked availability.

Which offers better value, Olmo 3.1 32B Instruct or Sonar Deep Research?+

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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