Invoking Gemini

Delegate tasks to Google's Gemini models when they offer advantages over Claude.

When to Use Gemini

Image generation:

• Blog header images, illustrations, diagrams
• Style-guided image creation (risograph, editorial, etc.)
• Text rendering in images

Speech (TTS):

• Narration, voice-over, read-aloud with style direction per line
• A custom voice designed from a written description
• Two-speaker dialogue

Structured outputs:

• JSON Schema validation with property ordering guarantees
• Pydantic model compliance
• Strict schema adherence (enum values, required fields)

Cost optimization:

• Parallel batch processing (Gemini 3 Flash is lightweight)
• High-volume simple tasks

Multi-modal tasks:

• Image analysis with JSON output
• Video processing
• Audio transcription with structure

Setup

uv pip install requests pydantic

Credentials — Option A (recommended): Cloudflare AI Gateway

Source /mnt/project/proxy.env with CF_ACCOUNT_ID, CF_GATEWAY_ID, CF_API_TOKEN. Requests route through Cloudflare AI Gateway, bypassing IP blocks. Google API key stored in gateway via BYOK.

Credentials — Option B: Direct Google API

If no proxy.env, falls back to direct: GOOGLE_API_KEY.txt or API_CREDENTIALS.json.

Image Generation

Generate images using Gemini's native image models. This is the primary way to create illustrations, blog headers, diagrams, and visual content.

Quick Start

import sys
sys.path.append('/mnt/skills/user/invoking-gemini/scripts')
from gemini_client import generate_image

# One call — returns {"path": "...", "caption": "..."} or None
result = generate_image("A watercolor painting of a mountain lake at sunset")
print(result["path"])  # /mnt/user-data/outputs/gemini_image_1740000000.png

Function Signature

generate_image(
    prompt: str,                    # The image description
    output_path: str = None,        # Auto-generates if omitted
    model: str = "nano-banana-2",   # Default: fast. Use "image-pro" for quality
    temperature: float = 0.7,       # 0.5-0.7 for diagrams, 0.7-0.8 for illustrations
) -> dict | None
# Returns: {"path": "/mnt/user-data/outputs/gemini_image_*.png", "caption": str|None}
# Returns None on failure

Model Selection

Alias	Model	Best For	Cost/image
"nano-banana-2" or "image"	gemini-3.1-flash-image-preview	Fast iteration, drafts	$0.067
"image-pro" or "nano-banana-pro"	gemini-3-pro-image-preview	Published content, text rendering	$0.134

Complete Blog Header Example

import sys
sys.path.append('/mnt/skills/user/invoking-gemini/scripts')
from gemini_client import generate_image

# 1. Compose prompt with style prefix + subject
style_prefix = (
    "Style: Risograph-inspired editorial illustration. "
    "Visible halftone dot texture and slight color misregistration between layers. "
    "Limited ink palette: deep indigo, warm coral, and sage green on off-white paper. "
    "Layered transparency where colors overlap creates rich secondary tones. "
    "Modern and professional — the aesthetic of an indie design studio, not a fantasy novel. "
    "Generous whitespace. No photorealism, no glow effects, no cyberpunk. No text or labels."
)
subject = "A raven perched on a stack of books, observing a network graph"
prompt = f"{style_prefix}\n\nSubject: {subject}. Wide landscape format, suitable as a blog header."

# 2. Generate (use image-pro for published content)
result = generate_image(prompt, model="image-pro", temperature=0.75)

if result:
    print(f"Saved: {result['path']}")
    # 3. Present to user
    # present_files([result["path"]])

Prompt Patterns

• Style prefix + subject: Prepend a style description, then describe the subject
• Be specific about style: "Risograph-inspired editorial illustration" not "a nice picture"
• Include composition: "Wide landscape format" / "centered, high contrast"
• Text rendering: "A poster with the text 'SALE' in bold red letters" (works well with image-pro)
• Negative constraints: "No photorealism, no glow effects" to avoid defaults

Custom Output Path

result = generate_image(
    "A logo for a coffee shop called 'Bean There'",
    output_path="/mnt/user-data/outputs/coffee_logo.png"
)

Speech Generation (TTS)

Gemini 3.8 Flash TTS and Flash-Lite TTS went GA on 2026-09-23. Output is WAV, 24 kHz mono 16-bit, SynthID-watermarked.

from gemini_client import generate_speech, design_voice, list_voices

r = generate_speech("Odin kept two ravens. <short pause> Huginn was thought.",
                    output_path="/tmp/line.wav", voice="Algenib",
                    style="quiet and dry, unhurried")
# {'path': '/tmp/line.wav', 'seconds': 4.2, 'audio_tokens': 135} or None

v = design_voice("A low, dry, quietly amused male voice with a faint rasp. "
                 "Soft southern British accent.", "narrator", gender="male",
                 language_code="en-GB")      # {'id': 'voice_...', 'sample_path': ...}
generate_speech("...", voice=v["id"])

lows = list_voices(gender="male", pitch="low")   # library of 2,089 prebuilt voices

• Voices: 30 studio voices (Charon, Kore, Algenib "gravelly", Enceladus "breathy", ...) plus 2,059 persona voices with ids like en-gb-storyteller-4. list_voices() returns accent, pitch, gender and a description for each; the accent filter needs the exact string ("Winchester English"), so filter accents on the returned field.
• Style: pass style= (a speech_metadata annotation). Do not prefix the text with "Style: ..." — the 3.8 models read the prefix aloud, and systemInstruction is rejected. Inline events go in the text: <laugh>, <sigh>, <breath>, <short pause>; CAPITALS stress a word.
• Designed voices are stored (1-year expiry, 200 per project). The description sets baseline delivery too: "thoughtful pauses" in it produced 1–1.8 s mid-line pauses that no per-line style removed.
• The model can change words. Seen in a 29-line narration: "Hmm, I get things wrong", "tell them" for "tell him". Anything with subtitles or a fixed script needs an ASR check (faster-whisper medium.en) and a retake.
• Cost: about 32 audio tokens per second of speech, $9/M through 2026-12-31 on 3.8 Flash TTS ($6/M Lite), so a minute is about $0.02.
• Voice replication (cloning from a 30 s sample plus a recorded consent clip) is not wired in, and is unavailable in the EEA, UK, Switzerland, India, Illinois and Texas.

Basic Text Usage

import sys
sys.path.append('/mnt/skills/user/invoking-gemini/scripts')
from gemini_client import invoke_gemini

response = invoke_gemini(
    prompt="Explain quantum computing in 3 bullet points",
    model="flash",  # gemini-3.8-flash (default)
)
print(response)

Structured Output

Use Pydantic models for guaranteed JSON Schema compliance:

from gemini_client import invoke_with_structured_output
from pydantic import BaseModel, Field

class BookAnalysis(BaseModel):
    title: str
    genre: str = Field(description="Primary genre")
    key_themes: list[str] = Field(max_length=5)
    rating: int = Field(ge=1, le=5)

result = invoke_with_structured_output(
    prompt="Analyze the book '1984' by George Orwell",
    pydantic_model=BookAnalysis
)
print(result.title)  # "1984"

Nested models are supported. Gemini's responseSchema rejects $ref/$defs, which pydantic emits for every nested model, so the client inlines them before sending:

class Finding(BaseModel):
    claim: str
    confidence: Literal["high", "medium", "low"]
    note: str | None = None

class Analysis(BaseModel):
    findings: list[Finding]     # nested — inlined for you
    gaps: list[str]

Budget output generously. Thinking tokens count against max_output_tokens (default 32768). Too low and the JSON truncates mid-object, which surfaces as a pydantic parse error rather than a length error — the client now detects finishReason=MAX_TOKENS and says so explicitly.

Parallel Invocation

from gemini_client import invoke_parallel

results = invoke_parallel(
    prompts=["Summarize Hamlet", "Summarize Macbeth", "Summarize Othello"],
    model="lite",  # gemini-3.5-flash-lite — cheap/fast tier for batch
)

Available Models

The current frontier Flash is gemini-3.8-flash (GA 2026-09-02), the default and the flash alias. Google shipped three Flash generations in six weeks: 3.6 (2026-07-21), 3.7 (2026-08-13), 3.8 (2026-09-02). Each stays callable under a pinned alias (flash-3.7, flash-3.6, flash-3.5, flash-3), and none has a shutdown date. gemini-3.1-flash-lite-preview from earlier docs is gone (shut down 2026-05-25).

The Pro tier is off routing. gemini-3.1-pro-preview costs 2.7× the input and 3.2× the output of 3.8 Flash at today's rates and loses to the 3.5+ Flash line on the coding and agentic benchmarks that matter here. Do not target it; the pro alias now resolves to gemini-3.8-flash, and "maximum reasoning" means thinking_level='high' on Flash.

Text / Reasoning Models

Model	Alias	Input/1M	Output/1M	Context	Notes
gemini-3.8-flash	flash	$0.75 → $1.50	$3.75 → $7.50	1M in / 64K out	Default. GA 2026-09-02. Current frontier Flash. Vs 3.7: Terminal-Bench 2.1 90.8% vs 81.6%, SWE-Bench Pro 61.6% vs 60.4%, SWE-Atlas 51.9% vs 48.0%, HLE flat (45.4% vs 45.7%). Google says it "works harder" at higher effort, so expect more thinking tokens per task. thinking_level is low/medium/high only — minimal returns HTTP 400 and the client downgrades it to low. Default medium spent 79 thinking tokens on a one-word reply (measured 2026-09-03); pass low for non-reasoning tasks.
gemini-3.7-flash	flash-3.7	$0.75 → $1.50	$3.75 → $7.50	1M / 64K	GA 2026-08-13. DeepSWE v1.1 65.3% vs 49.0% on 3.6, Terminal-Bench 2.1 85.8%. Same minimal restriction as 3.8. Google keeps it "fully supported for efficiency-first workloads".
gemini-3.6-flash	flash-3.6	$0.75 → $1.50	$3.75 → $7.50	1M / 64K	GA 2026-07-21. ~17% fewer output tokens than 3.5 Flash. Last Flash that accepts thinking_level='minimal' (verified 2026-09-03).
gemini-3.5-flash	flash-3.5	$1.50	$9.00	1M	GA 2026-05-19. Google's model list now labels it "legacy". Accepts minimal. Costs more on output than 3.6–3.8.
gemini-3-flash-preview	flash-3	$0.30	$2.50	1M	Older preview Flash, kept for back compat. Google's listed migration target for it is gemini-3.6-flash; no shutdown date.
gemini-3.1-pro-preview	—	$2.00 (≤200K) / $4.00	$12.00 / $18.00	1M	DEPRECATED from routing (2026-09-03). Price/quality dominated by 3.6+ Flash; 3.5 Flash already beat it on most coding/agentic benchmarks. ID stays callable for pinned code. pro now resolves to gemini-3.8-flash. 3.5 Pro was announced at I/O 2026-05-19 for June and is still absent from the API as of 2026-09-03; it gets the same price/quality test before any alias points at it.
gemini-3.5-flash-lite	lite	$0.30	$2.50	1M	Cheap/bulk tier. GA 2026-07-21. Fastest 3.5-class (350 output tok/sec); beats gemini-3-flash on SWE-Bench Pro and OSWorld-Verified.
gemini-2.5-flash	stable-flash	$0.30	$2.50	1M	DEPRECATED — 2025-era generation, do not route here.
gemini-2.5-flash-lite	—	$0.10	$0.40	1M	DEPRECATED — cheaper, but a 2025-era generation. lite now resolves to gemini-3.5-flash-lite.
gemini-2.5-pro	stable-pro	$1.25 (≤200K) / $2.50	$10.00 / $20.00	1M	DEPRECATED — 2025-era generation, do not route here.

$0.75 → $1.50 means introductory pricing: Google's pricing page (fetched 2026-09-03) lists 3.6, 3.7 and 3.8 Flash at $0.75 in / $3.75 out through 2026-12-31 and $1.50 / $7.50 from 2027-01-01. Context caching is $0.075 → $0.15; Batch is half of standard. Output prices include thinking tokens.

Image Models

Model	Alias	Input/1M	Per Image
gemini-3.1-flash-image-preview	image, nano-banana-2	$0.25	$0.067
gemini-3-pro-image-preview	image-pro, nano-banana-pro	$2.00	$0.134

Speech Models

Model	Alias	Input/1M	Output/1M (audio)	Notes
gemini-3.8-flash-tts	tts	$0.50 → $1.00	$9.00 → $18.00	GA 2026-09-23. Expressive, 130 languages, 2 speakers. Use generate_speech(), not invoke_gemini().
gemini-3.8-flash-lite-tts	tts-lite	$0.50 → $1.00	$6.00 → $12.00	GA 2026-09-23. Bulk / read-aloud, 101 languages. Replaces gemini-3.1-flash-tts-preview ($1 / $20).

Speech aliases live in SPEECH_ALIASES, not MODEL_ALIASES, so a text call can never resolve to an audio model.

See references/models.md for full details.

Thinking Budget (Gemini 3.x)

Gemini 3.x models reason before responding. The parameter changed in 2026: integer thinking_budget is gone; use string thinking_level ∈ {minimal, low, medium, high}. Default for 3.5–3.8 Flash is medium. For transcription / classification / extraction tasks, pass thinking_level='minimal' or the model will silently spend output tokens on reasoning (symptom: empty response with finishReason=MAX_TOKENS).

3.7 and 3.8 Flash reject minimal with HTTP 400 (Thinking level MINIMAL is not supported for this model); low is their floor. The client downgrades minimal to low on those two models and prints a note to stderr, so existing callers keep working. Measured on 3.8 (2026-09-03): low spent 0 thinking tokens on a one-word reply, the default medium spent 79. On 3.7, low still spent 45–88, and a max_output_tokens=50 call at low hit MAX_TOKENS and returned None, so budget output generously there. If a job needs a true no-thinking pass, pin flash-3.6 or lite, which still accept minimal.

response = invoke_gemini(
    prompt="Transcribe this image.",
    model="flash",
    image_path="/tmp/screenshot.png",
    max_output_tokens=4000,
    thinking_level="minimal",  # don't burn output budget on reasoning
)

Error Handling

response = invoke_gemini(prompt="...", model="flash")
if response is None:
    print("API call failed — check credentials")

result = generate_image("...")
if result is None:
    print("Image generation failed — check credentials or try again")

Common issues: Missing API key → see Setup. Rate limit → auto-retries with backoff. Network error → returns None.

Advanced Features

Custom Generation Config

response = invoke_gemini(
    prompt="Write a haiku",
    model="flash",                  # gemini-3.8-flash
    temperature=0.9,
    max_output_tokens=200,
    top_p=0.95,
    thinking_level="low",           # haiku is short; modest reasoning is fine
)

Multi-modal Input

from pydantic import BaseModel
from gemini_client import invoke_with_structured_output

class ImageDescription(BaseModel):
    objects: list[str]
    scene: str
    colors: list[str]

result = invoke_with_structured_output(
    prompt="Describe this image",
    pydantic_model=ImageDescription,
    image_path="/mnt/user-data/uploads/photo.jpg"
)

See references/advanced.md for more patterns.

Troubleshooting

"No credentials configured": Create /mnt/project/proxy.env with CF credentials, or add GOOGLE_API_KEY.txt.

CF Gateway 401/403: Verify CF_API_TOKEN has AI Gateway permissions. If not using BYOK, add GOOGLE_API_KEY to proxy.env.

Import errors: uv pip install requests pydantic

Image generation returns None: Check credentials. If persistent, try model="nano-banana-2" (more reliable than image-pro). Check for content policy blocks in error output.