Compare

Eleven V3 vs Gemini 2.5 Pro

Pricing per million tokens, context window, capabilities — pulled from each provider's public docs. All 2 are available via the same AIgateway OpenAI-compatible endpoint; flip the model string to switch.

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Eleven V3
elevenlabs/eleven-v3
Gemini 2.5 Pro
google/gemini-2.5-pro
Provider
Elevenlabs
Google
Family
Modality
audio-tts
text
Context window
1,000,000 tok
Max output
65,536 tok
Released
2026-07-24
2026-05-22
License
Open-weight
Proprietary
Input price
$1.25 /1M
Output price
$10.00 /1M
Per 1K chars
$0.0001 /1K ch
Tools
Streaming
yes
Vision
yes
JSON mode
Reasoning
Prompt caching
Batch API
Try it
View model →
Open in playground →
Eleven V3
elevenlabs/eleven-v3
Full spec →

ElevenLabs' latest text-to-speech model for highly expressive, natural speech generation with advanced voice control.

Strengths
  • Natural speech synthesis
Use cases
VoiceoversIVRAudiobooks
Gemini 2.5 Pro
google/gemini-2.5-pro
Full spec →

Google's most capable Gemini 2.5 model with strong reasoning, thinking support, and a 1M token context window.

Strengths
  • General-purpose chat
  • Long context
  • Tool use
Use cases
ChatbotsContent generationAgentic workflows

Compare with another

Claude Opus 5 vs Gemini 2.5 Pro
anthropic/claude-opus-5 · google/gemini-2.5-pro
Claude Opus 5 vs Eleven V3
anthropic/claude-opus-5 · elevenlabs/eleven-v3
Grok 4.5 vs Gemini 2.5 Pro
xai/grok-4.5 · google/gemini-2.5-pro
Grok 4.5 vs Eleven V3
xai/grok-4.5 · elevenlabs/eleven-v3
Kimi K3 vs Gemini 2.5 Pro
moonshot/kimi-k3 · google/gemini-2.5-pro
Kimi K3 vs Eleven V3
moonshot/kimi-k3 · elevenlabs/eleven-v3
Gemini 3.6 Flash vs Gemini 2.5 Pro
google/gemini-3.6-flash · google/gemini-2.5-pro
Gemini 3.6 Flash vs Eleven V3
google/gemini-3.6-flash · elevenlabs/eleven-v3
GPT-5.6 Sol vs Gemini 2.5 Pro
openai/gpt-5.6-sol · google/gemini-2.5-pro
SWITCH BETWEEN THEM

One key, all 2, one line different.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.aigateway.sh/v1",
    api_key="sk-aig-...",
)

# Eleven V3
client.chat.completions.create(
    model="elevenlabs/eleven-v3",
    messages=[{"role":"user","content":"hello"}],
)

# Gemini 2.5 Pro
client.chat.completions.create(
    model="google/gemini-2.5-pro",
    messages=[{"role":"user","content":"hello"}],
)
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