Gemini 3.1 Pro API Pricing: Deep Multimodal Reasoning and 2M Scale
Comprehensive Gemini 3.1 Pro API pricing analysis ($2.00/M input, $12.00/M output), 2M context window economics, multimodal vision/audio benchmarks, and enterprise SLAs.
Full specs, context window and API limits →How much does Gemini 3.1 Pro cost per million tokens?
Gemini 3.1 Pro costs $2.00 per million input tokens and $12.00 per million output tokens ($4.50/M blended at 3:1). Google flagship cognitive model designed for complex multi-modal analysis, long-context research, and high-precision STEM tasks. Verified 2026-09-08.
Verified 2026-09-07 — source
How much does Gemini 3.1 Pro cost per 1,000 requests?
Computed from generated token pricing. Each row assumes the listed input and output tokens per request; output is adjusted by this model's measured 0.63× verbosity factor.
| Request shape | Input tokens | Output tokens | Cost / 1,000 requests |
|---|---|---|---|
| Short | 100 | 50 | $0.5780 |
| Medium | 1,000 | 500 | $5.7800 |
| Long | 4,000 | 2,000 | $23.1200 |
Formula: ((input price × input tokens) + (output price × output tokens × verbosity factor)) ÷ 1,000,000 × 1,000. Assumptions: short 100/50, medium 1,000/500, long 4,000/2,000 input/output tokens per request. Verbosity run: 2026-06-21T00:00:00.000Z.
Batch 61 · exact-model pricing decision contributions · verified 2026-09-07
Exact model boundary: Google Gemini 3.1 Pro (gemini-3.1-pro). Pricing cards, context tiers, caching multipliers, and task pages remain fact owners.
Tier 1 vs Tier 2 context threshold billing ledger
Frozen Batch 61 scenario board. Formula / deterministic rule: rate = tokens <= 128000 ? tier1_rates : tier2_rates; tier 2 doubles input/output rates Boundary: Owns context window threshold economics for Gemini 3.1 Pro.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch61-gemini-3-1-pro-m1-r1standard 32K prompt | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=standard 32K prompt; token count; context tier; input rate; output rate; request cost; tier delta; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — standard 32K prompt is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m1-r2document extraction (96K) | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=document extraction (96K); token count; context tier; input rate; output rate; request cost; tier delta; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — document extraction (96K) is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m1-r3tier boundary (128K) | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=tier boundary (128K); token count; context tier; input rate; output rate; request cost; tier delta; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — tier boundary (128K) is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m1-r4large codebase review (350K) | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=large codebase review (350K); token count; context tier; input rate; output rate; request cost; tier delta; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — large codebase review (350K) is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m1-r5massive context dataset (1M) | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=massive context dataset (1M); token count; context tier; input rate; output rate; request cost; tier delta; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — massive context dataset (1M) is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m1-r6extreme context analysis (2M) | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=extreme context analysis (2M); token count; context tier; input rate; output rate; request cost; tier delta; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — extreme context analysis (2M) is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
First-party provenance: Google Gemini API pricing; verification date 2026-09-07. Missing or conflicting joins fail closed.
Native multimodal video, audio & image ingestion costs
Frozen Batch 61 scenario board. Formula / deterministic rule: media_cost = video_sec * video_rate + audio_sec * audio_rate + images * image_rate + text_tokens * token_rate Boundary: Owns multimodal token ingestion economics.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch61-gemini-3-1-pro-m2-r1scanned PDF images (50 pages) | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=scanned PDF images (50 pages); media type; duration/count; token equivalent; unit rate; calculated cost; verification status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — scanned PDF images (50 pages) is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m2-r215-minute meeting audio QA | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=15-minute meeting audio QA; media type; duration/count; token equivalent; unit rate; calculated cost; verification status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 15-minute meeting audio QA is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m2-r31-hour video lecture understanding | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=1-hour video lecture understanding; media type; duration/count; token equivalent; unit rate; calculated cost; verification status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — 1-hour video lecture understanding is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m2-r4mixed text and video dataset | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=mixed text and video dataset; media type; duration/count; token equivalent; unit rate; calculated cost; verification status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — mixed text and video dataset is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m2-r5high-resolution engineering diagram | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=high-resolution engineering diagram; media type; duration/count; token equivalent; unit rate; calculated cost; verification status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — high-resolution engineering diagram is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m2-r6unsupported media container | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=unsupported media container; media type; duration/count; token equivalent; unit rate; calculated cost; verification status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — unsupported media container has no matched, dated bilateral observation. | FAIL CLOSED — manual, probe, or source evidence required |
First-party provenance: Google Gemini model guide; verification date 2026-09-07. Missing or conflicting joins fail closed.
AI Studio vs Vertex AI enterprise deployment economics
Frozen Batch 61 scenario board. Formula / deterministic rule: vertex_spend = base_tokens_cost + enterprise_addons; token tariffs match published API Boundary: Owns enterprise infrastructure routing costs for Google serving.
| Frozen scenario / field ID | Exact identity and evidence fields | Result | State |
|---|---|---|---|
batch61-gemini-3-1-pro-m3-r1standard developer project | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=standard developer project; endpoint realm; tool surcharges; quota tier; search grounding add-on; monthly estimate; status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — standard developer project is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m3-r2Vertex AI enterprise project | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=Vertex AI enterprise project; endpoint realm; tool surcharges; quota tier; search grounding add-on; monthly estimate; status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — Vertex AI enterprise project is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m3-r3grounding with Google Search | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=grounding with Google Search; endpoint realm; tool surcharges; quota tier; search grounding add-on; monthly estimate; status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — grounding with Google Search is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m3-r4code execution environment run | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=code execution environment run; endpoint realm; tool surcharges; quota tier; search grounding add-on; monthly estimate; status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — code execution environment run is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m3-r5provisioned throughput reservation | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=provisioned throughput reservation; endpoint realm; tool surcharges; quota tier; search grounding add-on; monthly estimate; status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — provisioned throughput reservation is a frozen fixture pending exact identity, configuration, and denominator joins. | UNTESTED — assumption cannot establish a verdict |
batch61-gemini-3-1-pro-m3-r6unresolved billing tier | model=gemini-3.1-pro; provider=Google; slug=gemini-3-1-pro; scenario=unresolved billing tier; endpoint realm; tool surcharges; quota tier; search grounding add-on; monthly estimate; status; prompt/config/input/output/result/cache/checkpoint/artifact hashes=required; evidence=2026-09-07; measurement versus assumption=explicit | Unavailable — unresolved billing tier has no matched, dated bilateral observation. | FAIL CLOSED — manual, probe, or source evidence required |
First-party provenance: Google Gemini API pricing; verification date 2026-09-07. Missing or conflicting joins fail closed.
Method and limitations: formulas are deterministic; observed and assumed inputs are labeled; no missing provider, host, account, region, realm, alias, snapshot, revision, weight, artifact, control, tool, modality, workload, rate-period, timestamp, or result is transferred. Run the Gemini 3.1 Pro Batch 61 scenario →
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gemini-3-1-proGemini 3.1 Pro API Pricing: Deep Multimodal Reasoning and 2M Scale
Gemini 3.1 Pro costs $2.00 per million input tokens and $12.00 per million output tokens ($4.50/M blended at 3:1). Google flagship cognitive model designed for complex multi-modal analysis, long-context research, and high-precision STEM tasks. Verified 2026-09-08.
Module 1 · Gemini 3.1 Pro Multimodal Flagship Token Rate Card
Blended Cost = (Input Tokens × $2.00 + Output Tokens × $12.00) / 1,000,000
Gemini 3.1 Pro combines elite reasoning with native multi-hour video and audio comprehension.
Boundary: Standard pay-as-you-go pricing for prompts <= 128K; prompts > 128K priced at extended tier.
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | Complex multi-document legal discovery (32K in, 4K out): $0.112000 per document pack |
| Scenario 2 | Medical diagnostic imaging critique (16K in, 2K out): $0.056000 per diagnostic pass |
| Scenario 3 | Hour-long video lecture multimodal analysis (80K in, 5K out): $0.220000 per lecture |
| Scenario 4 | Advanced algebraic topology proof derivation (8K in, 3K out): $0.052000 per proof run |
| Scenario 5 | Full software design specification drafting (24K in, 4K out): $0.096000 per design document |
| Scenario 6 | Monthly 50M token cognitive research workload: $225.00 infrastructure budget |
Module 2 · Gemini 3.1 Pro 2M Context Caching & Corpus Amortization
Cached Cost = (Cached Input × $0.50 + Uncached Input × $2.00 + Output × $12.00) / 1,000,000
Context caching enables affordable, fluid conversational exploration of deep video and document archives.
Boundary: 75% discount on prompt prefixes >1,024 tokens held in Google AI Studio / Vertex AI cache.
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | Cached multi-video training library (250K tokens, 5K query): 71% input cost savings |
| Scenario 2 | Large enterprise document repository cache (500K tokens): $0.25000 vs $1.00000 per query |
| Scenario 3 | Interactive research dialogue over 1M token archive: 73% cumulative input savings |
| Scenario 4 | Hourly storage fee ($4.00/M/hr) amortized after only 3 queries per hour |
| Scenario 5 | Time-to-first-token cut by 50% by avoiding repetitive multimodal prompt encoding |
| Scenario 6 | Enables interactive real-time research over massive multimedia archives |
Module 3 · Gemini 3.1 Pro vs Gemini 3.7 Flash Fleet Architecture
Fleet Efficiency = (0.85 × 3.7 Flash Spend) + (0.15 × 3.1 Pro Spend)
Pairing 3.7 Flash for velocity with 3.1 Pro for deep video reasoning cuts multimodal bills by 56%.
Boundary: Evaluates savings from routing routine tasks to 3.7 Flash and reserving 3.1 Pro for deep analysis.
| Scenario | Rendered Evidence & Bounds |
|---|---|
| Scenario 1 | 1M queries routed via tiered architecture: $1,950.00 vs $4,500.00 monolithic Pro fleet |
| Scenario 2 | Gemini 3.7 Flash ($0.75/$3.75) absorbs 85% high-speed multimodal extraction and chat |
| Scenario 3 | Gemini 3.1 Pro ($2.00/$12.00) handles 15% complex multi-hour video analysis and formal proofs |
| Scenario 4 | Fleet average response latency drops by 60% due to Flash sub-second generation speed |
| Scenario 5 | Enterprise cost savings exceed 56.6% compared to routing all traffic to 3.1 Pro |
| Scenario 6 | Seamless Vertex AI / Google AI Studio integration allows uniform SDK request formats |
How fast is Gemini 3.1 Pro?
Tokens / sec
55
TTFT
420 ms
Rank
#26 of 31
$ / M ÷ t/s
$0.08
Measured with 5 runs on a fixed prompt — see the full methodology.
How much does Gemini 3.1 Pro cost at scale?
| Tokens / month | Est. cost (blended 3:1) |
|---|---|
| 100,000 | $0.45 |
| 1,000,000 | $4.50 |
| 10,000,000 | $45.00 |
| 100,000,000 | $450.00 |
How does Gemini 3.1 Pro compare with other models?
Gemini 2.5 Flash Lite — $0.18/MGemini 3.1 Flash Lite — $0.56/MGemini 3.5 Flash Lite — $0.85/MGemini 2.5 Flash — $0.85/MGemini 3.7 Flash — $1.50/MGPT-4o — $4.38/MClaude Sonnet 5 — $4.00/MGPT-4.1 — $3.50/M
What is Gemini 3.1 Pro best for?
What should you explore next for Gemini 3.1 Pro?
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GPT-OSS 120B (Cerebras) is 90% cheaper — a config migration. See all 8 alternatives to Gemini 3.1 Pro →
Which Gemini 3.1 Pro head-to-head comparisons are available?
Gemini 3.1 Pro vs Claude Opus 4.8Gemini 3.1 Pro vs Claude Sonnet 5Gemini 3.1 Pro vs DeepSeek V4 ProGemini 3.1 Pro vs GPT-5.6 Sol
What are common questions about Gemini 3.1 Pro?
Is Gemini 3.1 Pro cheaper than GPT-4o?
Gemini 3.1 Pro costs $4.50/M blended tokens, GPT-4o costs $4.38/M — GPT-4o is cheaper.
How much does 1 million tokens cost with Gemini 3.1 Pro?
At a 3:1 input:output ratio, 1 million blended tokens costs approximately $4.50. Pure input costs $2.00/M; pure output costs $12.00/M.
What does Gemini 3.1 Pro cost at high volume?
At 100 million blended tokens a month, Gemini 3.1 Pro costs approximately $450.00. See the cost-at-scale table below for other volumes.