Google DeepMind

Gemini 3.8 Flash

Tool callingStructured outputReasoningImage inputAudio inputVideo inputFile input
Gemini 3.8 Flash launch post cover

Google's most intelligent Flash model, engineered for long-horizon software engineering, autonomous agents, and complex enterprise workflows

Context
1.0M
Released
1mo ago
Input / 1M tokens
$0.75
Output / 1M tokens
$3.75
Cached input / 1M tokens
$0.08
Cache write / 1M tokens
$0.04

Sources:models.devOpenRouter

Benchmarks

DeepSWE is the primary coding benchmark where an exact model evaluation exists. Other sourced evaluations appear below; missing scores are not zero.

DeepSWE 1.1

Primary coding-agent benchmark · 113 tasks · Data generated 2026-09-22

Official leaderboard
73.8%
pass@1 · highest published configuration
mini-swe-agent · high reasoning
pass@4
85.8%
Average cost / attempt
$2.36
Benchmark runs
4

All published configurations

Harness / reasoningpass@1pass@4Cost / attempt
mini-swe-agent · high73.8%85.8%$2.36
mini-swe-agent · medium71.0%83.2%$1.97

pass@1 is the success rate across scored attempts; pass@4 is the share of tasks solved in at least one of four attempts. Costs are reported by DeepSWE for each configuration. These results are not directly comparable to evaluations using different harnesses. Official source JSON.

Other published benchmarks

EvaluationReported score and source
ARC-AGI-2
89.2%
Third-party evaluator · gemini-3.8-flash_high
Reasoning effort: high
External evaluation via Epoch AI
Original-source terms apply
ARC-AGI-2
77.5%
Third-party evaluator · gemini-3.8-flash_low
Reasoning effort: low
External evaluation via Epoch AI
Original-source terms apply
ARC-AGI-2
82.9%
Third-party evaluator · gemini-3.8-flash_medium
Reasoning effort: medium
External evaluation via Epoch AI
Original-source terms apply
FrontierMath-Tier-4-v2-Private
22.0%
Epoch AI · gemini-3.8-flash_high
Reasoning effort: high
Epoch AI evaluation
FrontierMath-Tiers-1-3-v2-Private
68.4%
Epoch AI · gemini-3.8-flash_high
Reasoning effort: high
Epoch AI evaluation
GPQA diamond
95.4%
Epoch AI · gemini-3.8-flash_high
Reasoning effort: high
Epoch AI evaluation
OTIS Mock AIME 2024-2025
98.9%
Epoch AI · gemini-3.8-flash_high
Reasoning effort: high
Epoch AI evaluation
SimpleQA Verified
69.7%
Epoch AI · gemini-3.8-flash_high
Reasoning effort: high
Epoch AI evaluation

Benchmark compilation: Epoch AI, Capabilities & benchmarking (CC BY attribution). Scores are converted to percentages and model names matched to the Vertical Tabs catalog. Individual evaluations are credited to their original sources. External results may carry separate licences and evaluation configurations; do not interpret scores from different harnesses as directly comparable.

Specifications

Model family
Gemini
Type
Language model
Predecessor
—
Successor
—
Built on
Released
September 2, 2026
Model ID
google/gemini-3.8-flash
Openness
Closed
License
—
Input modalities
text, image, video, file, audio
Output modalities
text
Modality
text+image+file+audio+video->text
Tokenizer
Gemini
Max output tokens
66K
Hugging Face
—
Source freshness
Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
Source model
google/gemini-3.8-flash
Source family
gemini-flash
Last updated
2026-09-02
Model context
1.0M
Model output limit
66K
Open weights
No
File attachments
Yes
Reasoning
Yes
Tool calling
Yes
Structured output
Yes
Temperature control
Yes

Published benchmarks

BenchmarkScoreEvaluation detailsEvidence
DeepSWE73.7 · pass@1mini-swe-agent · high thinking · 1.1Source
GDPval-AA1545 · Elo2Source
Finance Agent61.4 · pass@12Source
Harvey Legal Agent Benchmark10 · all-pass rate—Source
Terminal-Bench89.4 · pass@1Terminus 2 · 2.1Source
Terminal-Bench19.1 · pass@14.0Source
GDP.PDF35 · all-pass rate—Source
CharXiv Reasoning86.2 · pass@1without toolsSource
LVBench87.8 · pass@1agenticSource
LVBench87.1 · pass@1static; 1024 frames; without toolsSource
HLE-Verified54.9 · accuracy1811 verified and revised itemsSource
OSWorld59 · partial scoreGemini CUA · before 2026-08-08 patch · batched tool calls; best of 3 runs; 500 steps · 2.0Source
BioMysteryBench88.8 · pass@1human-solvable · with toolsSource
BioMysteryBench56.5 · pass@1human-difficult · with toolsSource
LAB-Bench86.2 · macro-average11 subtasks · with tools · 2Source
Complete source record (JSON)
{
  "id": "google/gemini-3.8-flash",
  "name": "Gemini 3.8 Flash",
  "description": "Google's most intelligent Flash model, engineered for long-horizon software engineering, autonomous agents, and complex enterprise workflows",
  "family": "gemini-flash",
  "last_updated": "2026-09-02",
  "attachment": true,
  "reasoning": true,
  "tool_call": true,
  "structured_output": true,
  "temperature": true,
  "modalities": {
    "input": [
      "text",
      "image",
      "video",
      "audio",
      "pdf"
    ],
    "output": [
      "text"
    ]
  },
  "limit": {
    "context": 1048576,
    "output": 65536
  },
  "benchmarks": [
    {
      "name": "DeepSWE",
      "score": 73.7,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "pass@1",
      "harness": "mini-swe-agent",
      "variant": "high thinking",
      "version": "1.1"
    },
    {
      "name": "GDPval-AA",
      "score": 1545,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "Elo",
      "version": "2"
    },
    {
      "name": "Finance Agent",
      "score": 61.4,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "pass@1",
      "version": "2"
    },
    {
      "name": "Harvey Legal Agent Benchmark",
      "score": 10,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "all-pass rate"
    },
    {
      "name": "Terminal-Bench",
      "score": 89.4,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "pass@1",
      "harness": "Terminus 2",
      "version": "2.1"
    },
    {
      "name": "Terminal-Bench",
      "score": 19.1,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "pass@1",
      "version": "4.0"
    },
    {
      "name": "GDP.PDF",
      "score": 35,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "all-pass rate"
    },
    {
      "name": "CharXiv Reasoning",
      "score": 86.2,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "pass@1",
      "variant": "without tools"
    },
    {
      "name": "LVBench",
      "score": 87.8,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "pass@1",
      "variant": "agentic"
    },
    {
      "name": "LVBench",
      "score": 87.1,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "pass@1",
      "variant": "static; 1024 frames; without tools"
    },
    {
      "name": "HLE-Verified",
      "score": 54.9,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "accuracy",
      "dataset": "1811 verified and revised items"
    },
    {
      "name": "OSWorld",
      "score": 59,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "partial score",
      "harness": "Gemini CUA",
      "variant": "batched tool calls; best of 3 runs; 500 steps",
      "dataset": "before 2026-08-08 patch",
      "version": "2.0"
    },
    {
      "name": "BioMysteryBench",
      "score": 88.8,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "pass@1",
      "variant": "with tools",
      "dataset": "human-solvable"
    },
    {
      "name": "BioMysteryBench",
      "score": 56.5,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "pass@1",
      "variant": "with tools",
      "dataset": "human-difficult"
    },
    {
      "name": "LAB-Bench",
      "score": 86.2,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/",
      "metric": "macro-average",
      "variant": "with tools",
      "dataset": "11 subtasks",
      "version": "2"
    }
  ],
  "release_date": "2026-09-02",
  "open_weights": false
}

Infrastructure

Provider availability

All providers
ProviderEndpointContextInput /M tokensOutput /M tokensCached input /M tokensCache write /M tokensUptime 1dData handling
Googlegoogle-vertex/global/flex1.0M$0.38$1.88$0.04$0.0293.42%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Googlegoogle-vertex/global/flexBatch pricing1.0M$0.38$1.88$0.04$0.0294.77%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Google AI Studiogoogle-ai-studio/flex1.0M$0.38$1.88$0.04$0.02100.00%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Google AI Studiogoogle-ai-studio/flexBatch pricing1.0M$0.38$1.88$0.04$0.02100.00%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Googlegoogle-vertex/global1.0M$0.75$3.75$0.08$0.0494.51%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Googlegoogle-vertex/globalBatch pricing1.0M$0.75$3.75$0.08$0.0495.97%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Google AI Studiogoogle-ai-studio1.0M$0.75$3.75$0.08$0.04100.00%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Google AI Studiogoogle-ai-studioBatch pricing1.0M$0.75$3.75$0.08$0.04100.00%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Googlegoogle-vertex/global/priority1.0M$1.35$6.75$0.14$0.0893.75%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Googlegoogle-vertex/global/priorityBatch pricing1.0M$1.35$6.75$0.14$0.0893.28%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Google AI Studiogoogle-ai-studio/priority1.0M$1.35$6.75$0.14$0.0894.09%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Google AI Studiogoogle-ai-studio/priorityBatch pricing1.0M$1.35$6.75$0.14$0.0894.31%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Provider data:OpenRouter

Text provenance

Gemini app/web

Google SynthID text watermarking

Google DeepMind says text generated in the Gemini app and web experience is watermarked with SynthID by adjusting token probabilities.

Official explanation