What are the verified coding and reasoning benchmarks for Gemini 3.8 Flash?

Gemini 3.8 Flash achieves SWE-bench Verified: 68.4% (Thinking (Low)), LiveCodeBench: 53.8%, AIME: 84%. Verified sources: Gemini Technical Specifications & Flash Evaluations (Google DeepMind).

How does Gemini 3.8 Flash compare to rival frontier coding models on SWE-bench and pricing?

Gemini 3.8 Flash achieves SWE-bench Verified: 68.4% at $0.15/$0.6 per 1M tokens. Direct peer comparison: DeepSeek V4.1 Flash (56.2% SWE-bench, $0.3/$0.9); Celeris-1 (50% SWE-bench, $0.1/$0.4); Muse Glimmer (48% SWE-bench, $0.08/$0.32); Gemini 3.5 Flash-Lite (52% SWE-bench, $0.075/$0.3).

Does Swarm isolate Gemini 3.8 Flash code edits from other agents?

Yes. Swarm provisions isolated Git worktrees for every session and subagent, preventing file race conditions and keeping your main working tree clean until verification passes.

Can Gemini 3.8 Flash be assigned to specific subagent roles?

Yes. In ~/.swarm/config.json, you can configure Gemini 3.8 Flash as Primary/Planner (DAG task decomposition), Coder (implementation in isolated worktrees), or Finder (read-only symbol search).

Gemini 3.8 Flash — Specs, Context & Capabilities

Q: What is the recommended Swarm multi-agent topology for Gemini 3.8 Flash?

Swarm recommends Gemini 3.8 Flash as Coder Subagent (Tier 3: High-Throughput Subagent Engine). Because Gemini 3.8 Flash is 3x faster and 20x cheaper than frontier pro models, it is the ideal engine for parallel Coder and Finder workers running concurrently inside separate Git worktrees. Best paired with: Claude 3.7 Sonnet as Lead Primary Planner.

Q: How do I run Gemini 3.8 Flash with Swarm?

Configure Gemini 3.8 Flash via CLI using: swarm ctl model set --provider google --model google/gemini-3.8-flash, or switch models interactively inside the terminal TUI with /models.

Gemini 3.8 Flash Benchmarks & Agentic Accuracy

Verified primary research & developer preference metrics

Primary Sources Verified

SWE-bench Verified

68.4%

Thinking (Low)Google DeepMind Technical Report ↗

LiveCodeBench

53.8%

Thinking (Low)LiveCodeBench Leaderboard ↗

LMSYS Coding Elo

1380Elo

Coding ArenaLMSYS Arena ↗

AIME 2024 / 2025

84%

ThinkingGoogle DeepMind Technical Report ↗

Pricing ($ / 1M tokens)

$0.15/ $0.6

$0.0375 cached readStandard API Pricing

What This Means in Real Software Development

Executes at ~160 tokens per second, cutting multi-turn agent response latency from 30 seconds down to 4 seconds.
Scores 68.4% on SWE-bench Verified with low thinking, trailing top frontier models by under 2% while costing 95% less.
Huge 1M context window enables ingesting entire repository schemas, dependency trees, and build logs simultaneously without middle-context truncation.
Extremely economical for parallel Swarm subagents: launching 5 parallel worktree coders costs pennies.

Swarm Multi-Agent Topology

Tier 3: High-Throughput Subagent EngineRole: Coder Subagent

Best Paired With: Claude 3.7 Sonnet as Lead Primary Planner

Because Gemini 3.8 Flash is 3x faster and 20x cheaper than frontier pro models, it is the ideal engine for parallel Coder and Finder workers running concurrently inside separate Git worktrees.

Git Worktree Isolation Advantage:

Swarms zero-lock worktree architecture allows running 4x or 8x Gemini 3.8 Flash subagents simultaneously without merge conflicts or file contention.

What Developers & Practitioners Are Saying

Real-world field observations, production strengths, and developer consensus

Field Consensus

"The ultimate high-speed utility engine in modern AI development. Featuring a 1M token window, it is the community's go-to choice for massive repository grep, search triage, and real-time validation."

Verified Real-World Strengths

Rapid throughput with sub-second time-to-first-token
Unbeatable economics ($0.15/M input, $0.60/M output, $0.0375/M cached read)
High recall across 1M+ token contexts without hallucinating file locations

Production Caveats & Gaps

SWE-bench score (46.2%) trails frontier reasoning models on deep logic puzzles
Can occasionally omit edge-case error handling in generated code snippets

Primary Sources & Methodology Citations

Gemini Technical Specifications & Flash Evaluations — Google DeepMind (2026)

Head-to-Head Frontier Comparisons: Gemini 3.8 Flash vs Rivals

Standardized, side-by-side benchmark & economics comparison against peer models

Flagship Comparative Matrix

Model	Role / Tier	SWE-bench	LiveCodeBench	LMSYS Elo	AIME	Input / Output
Gemini 3.8 FlashGoogle GeminiCurrent	Tier 3: High-Throughput Subagent Engine	68.4%	53.8%	1380	84%	$0.15 / $0.6
DeepSeek V4.1 FlashDeepSeek Compare Gemini 3.8 Flash vs DeepSeek V4.1 Flash →	Tier 3: Refined Open Subagent	56.2%	52%	1360	74.5%	$0.3 / $0.9
Celeris-1Independent Compare Gemini 3.8 Flash vs Celeris-1 →	Tier 3: World-Record Throughput Reflex	50%	43%	—	—	$0.1 / $0.4
Muse GlimmerMeta Compare Gemini 3.8 Flash vs Muse Glimmer →	Tier 3: Rapid Transpiler Subagent	48%	41.5%	1270	—	$0.08 / $0.32
Gemini 3.5 Flash-LiteGoogle Compare Gemini 3.8 Flash vs Gemini 3.5 Flash-Lite →	Tier 3: Extreme-Throughput Reflex Subagent	52%	45%	1295	—	$0.075 / $0.3

Explore All AI Model Comparisons Hub (Gemini 3.8 Flash & Flagship Peers) →

Gemini 3.8 Flash model overview

Gemini 3.8 Flash is listed in the public Swarm catalog through Google Gemini. This page summarizes the verified catalog fields used by Swarm without claiming real-time access for every provider account.

Provider: Google Gemini
Family: Gemini
Context window: 1.05M tokens
Thinking support: low · medium · high
Availability status: Available
Catalog ID: google/gemini-3.8-flash

Audio input
File input
Image input
Text input
Video input
Text output
Reasoning
Tools
Structured output
JSON
Batch

Run Gemini 3.8 Flash in Swarm

Verified CLI workflow & multi-agent architecture

Quickstart Guide →

bash — swarm run

$swarm ctl model set --provider google --model google/gemini-3.8-flash

# Or switch interactively inside the terminal TUI:

>/models

Subagent Role Matrix

Assign Gemini 3.8 Flash to specialized system roles in ~/.swarm/config.json:

Primary / Planner: Orchestrates full task DAGs and multi-stage pipelines.
Coder Subagent: Generates code and unit tests inside private Git worktrees.
Finder Subagent: Performs read-only code search, symbol discovery, and mapping.

System Agent Roles →Why Swarm Architecture →