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Google DeepMind · launched 19 Feb 2026

Gemini 3.1 Pro

Preview

Google's publicly available Pro model for complex reasoning, multimodal understanding and agentic coding, currently in preview.

Read the official announcement
Model details
Vendor
Google DeepMind
Launch date
19 Feb 2026
Input price
$2.00 / 1M tokens
Output price
$12 / 1M tokens
Cached input
$0.20 / 1M tokens
Batch discount
50%
Context window
1.05M tokens
Max output
66K tokens
Input types
Text, Image, Video, Audio, PDF
API model ID
gemini-3.1-pro-preview
Availability
Preview

Rates apply to prompts up to 200K tokens. Above 200K, input is $4, output $18 and cached input $0.40 per million tokens. Cache storage and grounding are billed separately.

Official pricing source · checked 8 Oct 2026

Official model specifications

Price compared with other models

ModelLaunchedInput / 1MOutput / 1MCached / 1MBlended / 1Mvs Gemini 3.1 Pro
Claude Haiku 5.5AnthropicListed prices and comparisons apply to prompts up to 100K tokens. Above 100K, input costs $0.50, output $2.50 and cache reads $0.05 per million tokens.7 Oct 2026$0.10$0.50$0.01$0.2096% cheaper
GPT-6 LunaOpenAI22 Sept 2026$0.10$0.50$0.01$0.2096% cheaper
GPT-5.6 LunaOpenAI9 Jul 2026$0.20$1.20$0.02$0.4590% cheaper
Gemini 3.5 Flash-LiteGoogle DeepMindText, image, video and audio inputs share the listed rate. Cache storage and grounding are billed separately.21 Jul 2026$0.30$2.50$0.03$0.8581% cheaper
Gemini 3.8 FlashGoogle DeepMindIntroductory rates through 31 December 2026. From 1 January 2027, input is $1.50, output $7.50 and cached input $0.15 per million tokens. Cache storage and grounding are billed separately.2 Sept 2026$0.75$3.75$0.075$1.5067% cheaper
Claude Haiku 4.5Anthropic15 Oct 2025$1.00$5.00$0.10$2.0056% cheaper
Grok 4.7SpaceXAIBase rates apply up to 200K context tokens; higher-context requests use different rates. Batch API is not supported. The separate fast variant costs twice as much.21 Sept 2026$2.00$6.00$0.50$3.0033% cheaper
Claude Sonnet 5Anthropic30 Jun 2026$2.00$10$0.20$4.0011% cheaper
Claude Sonnet 5.5AnthropicCache reads were reduced from $0.20 to $0.10 per million tokens on 7 October 2026.28 Sept 2026$2.00$10$0.10$4.0011% cheaper
Gemini 4 ArgonGoogle DeepMind · Limited accessAnnounced introductory rates for the upcoming broader launch. Regular rates will be $4 input and $20 output per million tokens; the introductory end date and batch discount are not published.30 Sept 2026$2.00$10$0.10$4.0011% cheaper
GPT-6 SolOpenAIListed rates apply up to 272K input tokens. Above 272K, input costs $4, cached input $0.40 and output $15 per million tokens for the full request.22 Sept 2026$2.00$10$0.20$4.0011% cheaper
GPT-6.1 SolOpenAIListed rates apply up to 272K input tokens. Above 272K, input costs $4, cached input $0.20 and output $15 per million tokens for the full request.29 Sept 2026$2.00$10$0.10$4.0011% cheaper
Gemini 3.1 ProGoogle DeepMind · PreviewRates apply to prompts up to 200K tokens. Above 200K, input is $4, output $18 and cached input $0.40 per million tokens. Cache storage and grounding are billed separately.19 Feb 2026$2.00$12$0.20$4.50Baseline
Claude Opus 5.5Anthropic22 Sept 2026$4.00$20$0.20$8.0078% more
GPT-5.6 SolOpenAIPrice before the GPT-6 launch, from OpenAI's announcement. OpenRouter now lists $2 / $10.9 Jul 2026$4.00$20–$8.0078% more
Claude Opus 5Anthropic24 Jul 2026$5.00$25$0.50$102.2× the price
Claude Fable 5Anthropic9 Jun 2026$10$50$1.00$204.4× the price
Claude Fable 5.1Anthropic1 Sept 2026$10$50$0.25$204.4× the price
GPT-6 AstraOpenAI4 Sept 2026$10$50$1.00$204.4× the price

GPT-6.1 Sol and GPT-6 Sol rates were checked on 8 October 2026 against OpenAI model documentation. USD per million tokens, checked 2 Oct 2026 via OpenRouter. Comparison uses the blended price (3 input tokens for every output token). Haiku 5.5 and Sonnet 5.5 prices were updated from Anthropic's 7 October 2026 announcement. Gemini and Grok prices were checked against official Google and xAI documentation on 8 October 2026.

Official benchmark results

Gemini 3.1 Pro launch results

Reported by Google DeepMind on 19 Feb 2026 · exact values

Exact published scores · Higher is better. Unreported results are omitted.

BenchmarkGemini 3.1 Pro (high)Gemini 3 Pro (high)Claude Sonnet 4.6 (max)Claude Opus 4.6 (max)GPT-5.2 (xhigh)GPT-5.3-Codex (xhigh)
Humanity's Last ExamAcademic reasoning (full set, text + MM) · No tools44.4%37.5%33.2%40%34.5%–
Humanity's Last ExamAcademic reasoning (full set, text + MM) · Search (blocklist) + Code51.4%45.8%49%53.1%45.5%–
ARC-AGI-2Abstract reasoning puzzles · ARC Prize Verified77.1%31.1%58.3%68.8%52.9%–
GPQA DiamondScientific knowledge · No tools94.3%91.9%89.9%91.3%92.4%–
Terminal-Bench 2.0Agentic terminal coding · Terminus-2 harness68.5%56.9%59.1%65.4%54%64.7%
Terminal-Bench 2.0Agentic terminal coding · Other best self-reported harness · (Codex)––––62.2%77.3%
SWE-Bench VerifiedAgentic coding · Single attempt80.6%76.2%79.6%80.8%80%–
SWE-Bench Pro (Public)Diverse agentic coding tasks · Single attempt54.2%43.3%––55.6%56.8%
LiveCodeBench ProCompetitive coding problems from Codeforces, ICPC, and IOI · Elo2887 Elo2439 Elo––2393 Elo–
SciCodeScientific research coding59%56%47%52%52%–
APEX-AgentsLong horizon professional tasks33.5%18.4%–29.8%23%–
GDPval-AA EloExpert tasks1317 Elo1195 Elo1633 Elo1606 Elo1462 Elo–
τ2-benchAgentic and tool use · Retail90.8%85.3%91.7%91.9%82%–
τ2-benchAgentic and tool use · Telecom99.3%98%97.9%99.3%98.7%–
MCP AtlasMulti-step workflows using MCP69.2%54.1%61.3%59.5%60.6%–
BrowseCompAgentic search · Search + Python + Browse85.9%59.2%74.7%84%65.8%–
MMMU-ProMultimodal understanding and reasoning · No tools80.5%81%74.5%73.9%79.5%–
MMMLUMultilingual Q&A92.6%91.8%89.3%91.1%89.6%–
MRCR v2 (8-needle)Long context performance · 128k (average)84.9%77%84.9%84%83.8%–
MRCR v2 (8-needle)Long context performance · 1M (pointwise)26.3%26.3%––––
View Google DeepMind's original charts
Google DeepMind: Gemini 3.1 Pro launch benchmark comparison.
Original chart published by Google DeepMind, shown for reference.
  • Bold marks the best reported score in each row.
  • Exact scores from the Google DeepMind model card. Gemini models use high thinking, Claude models use max thinking and GPT models use xhigh thinking.
  • Terminal-Bench results using the Terminus-2 harness and the best self-reported Codex harness are separate rows. Do not combine them as one evaluation.
  • Historical competitors are benchmark comparisons only. A dash means no published result or unsupported context length.
Source: Gemini 3.1 Pro model card (Google DeepMind)