Gemini 3.1 Pro API: 94.3% GPQA Diamond Score at $2/MTok — Full Review and Pricing (2026)

TokenMix Research Lab · 2026-04-17

Gemini 3.1 Pro API: 94.3% GPQA Diamond Score at $2/MTok -- Full Review and Pricing (2026)

Gemini 3.1 Pro is the best price-to-performance model at the flagship tier in April 2026. It scores 94.3% on GPQA Diamond and 80.6% on SWE-bench Verified -- both top-tier results. It costs $2 per million input tokens and $12 per million output tokens. That is 20% cheaper than GPT-5.4 ($2.50/$15) and 33% cheaper than Claude Sonnet 4.6 ($3/$15) on input. For the first time, the highest-scoring model on key benchmarks is also the cheapest flagship. This gemini 3.1 pro review covers benchmark data, gemini api pricing, real-world performance comparisons, cost calculations at scale, and specific use-case recommendations. All data tracked and verified by [TokenMix.ai](https://tokenmix.ai) as of April 2026.

[Quick Specs: Gemini 3.1 Pro at a Glance](#quick-specs)
[Gemini 3.1 Pro Benchmark Performance](#benchmarks)
[Gemini API Pricing: Full Cost Breakdown](#pricing)
[Gemini 3.1 Pro vs GPT-5.4 vs Claude Sonnet 4.6](#vs-competitors)
[Real-World Cost Comparison at Scale](#cost-comparison)
[Best Use Cases for Gemini 3.1 Pro](#use-cases)
[Limitations and Trade-offs](#limitations)
[Decision Guide: When to Choose Gemini 3.1 Pro](#decision-guide)
[Conclusion](#conclusion)
[FAQ](#faq)

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Quick Specs: Gemini 3.1 Pro at a Glance {#quick-specs}

| Spec | Gemini 3.1 Pro | | --- | --- | | **GPQA Diamond** | 94.3% | | **SWE-bench Verified** | 80.6% | | **Input Price** | $2.00/MTok | | **Output Price** | $12.00/MTok | | **Provider** | Google DeepMind | | **Context Window** | 1M+ tokens | | **Multimodal** | Text, Image, Video, Audio | | **Release Period** | Feb-Apr 2026 | | **Category** | Flagship |

One number tells the story: 94.3% GPQA Diamond at $2/MTok input. No other model matches that ratio of capability to cost.

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Gemini 3.1 Pro Benchmark Performance {#benchmarks}

GPQA Diamond: 94.3% -- The Headline Number

GPQA Diamond is a graduate-level science benchmark designed to test expert-level reasoning. Questions are written by domain PhDs and validated to be answerable only with genuine understanding, not pattern matching.

Gemini 3.1 Pro scores 94.3%. This is the highest publicly reported score on GPQA Diamond by any commercial model as of April 2026. It means Gemini 3.1 Pro answers 94 out of 100 PhD-level science questions correctly. For applications in research assistance, scientific analysis, medical information processing, and technical documentation, this is a direct measure of capability.

SWE-bench Verified: 80.6% -- Top-Tier Coding

SWE-bench Verified measures a model's ability to resolve real GitHub issues autonomously. Gemini 3.1 Pro scores 80.6%, placing it in the top tier alongside GPT-5.4 (~80%) and above Claude Sonnet 4.6 (~73%).

The practical meaning: in automated code generation and repair pipelines, Gemini 3.1 Pro resolves 4 out of 5 real-world coding issues. The 0.6-point lead over GPT-5.4 is within margin of error. The 7.6-point lead over Claude Sonnet 4.6 is not.

DeepSeek V4 claims a higher SWE-bench score (~81%), but at a fundamentally different price point ($0.30/$0.50) and with different reliability characteristics.

Benchmark Summary Table

| Benchmark | Gemini 3.1 Pro | GPT-5.4 | Claude Sonnet 4.6 | DeepSeek V4 | | --- | --- | --- | --- | --- | | **GPQA Diamond** | **94.3%** | ~88% | ~85% | ~82% | | **SWE-bench Verified** | **80.6%** | ~80% | ~73% | 48.2% | | **Category** | Flagship | Flagship | Flagship | Budget-Frontier | | **Input/MTok** | $2.00 | $2.50 | $3.00 | $0.30 |

Gemini 3.1 Pro leads on both GPQA Diamond and SWE-bench while being the cheapest flagship. That combination did not exist before this release.

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Gemini API Pricing: Full Cost Breakdown {#pricing}

Base Gemini API Pricing

| Component | Gemini 3.1 Pro | Gemini Flash-Lite | | --- | --- | --- | | **Input/MTok** | $2.00 | $0.25 | | **Output/MTok** | $12.00 | -- | | **Category** | Flagship | Budget |

Google's pricing strategy is clear: undercut OpenAI and Anthropic at every tier. Gemini 3.1 Pro at $2/$12 is 20% cheaper than GPT-5.4 on input and 20% cheaper on output. Against Claude Sonnet 4.6, the input savings are 33%.

Gemini API Pricing vs Competitor Flagships

| Cost Dimension | Gemini 3.1 Pro | GPT-5.4 | Claude Sonnet 4.6 | | --- | --- | --- | --- | | **Input/MTok** | **$2.00** | $2.50 | $3.00 | | **Output/MTok** | **$12.00** | $15.00 | $15.00 | | **Input Savings vs Gemini** | Baseline | +25% more expensive | +50% more expensive | | **Output Savings vs Gemini** | Baseline | +25% more expensive | +25% more expensive |

At scale, these differences compound. A team processing 100 million output tokens per month saves $300,000/month choosing Gemini 3.1 Pro over GPT-5.4 or Claude Sonnet 4.6.

Where Google Gets Its Pricing Edge

Google runs Gemini on custom TPU v6 hardware. Unlike OpenAI and Anthropic, which rent NVIDIA GPU capacity, Google owns and operates its own inference infrastructure. This vertical integration creates a structural cost advantage that competitors cannot easily replicate.

TokenMix.ai tracks gemini api pricing in real time. Google has historically maintained stable API pricing once set, unlike some providers who adjust rates after launch promotions expire.

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Gemini 3.1 Pro vs GPT-5.4 vs Claude Sonnet 4.6 {#vs-competitors}

Where Gemini 3.1 Pro Wins

**Science and reasoning.** GPQA Diamond 94.3% is the defining advantage. For any application requiring expert-level knowledge -- research tools, scientific analysis, medical information systems, advanced technical Q&A -- Gemini 3.1 Pro is the strongest option.

**Coding at lower cost.** SWE-bench 80.6% matches GPT-5.4 and beats Claude Sonnet 4.6, at 20-33% lower pricing. For coding-heavy workloads, Gemini 3.1 Pro delivers equivalent results for less money.

**Multimodal breadth.** Gemini 3.1 Pro handles text, image, video, and audio natively. GPT-5.4 supports text, image, and audio. Claude Sonnet 4.6 supports text and image only. For applications processing video or mixed media, Gemini has no flagship-tier competitor.

**Price.** At $2/$12, it is objectively the cheapest flagship. Full stop.

Where GPT-5.4 Wins

**Ecosystem and tooling.** OpenAI's developer ecosystem remains the largest. More SDKs, more tutorials, more production deployments. Switching costs are real.

**Reasoning models.** Access to o3 and o4-mini for dedicated reasoning tasks gives GPT-5.4 users options that Gemini's built-in thinking mode does not fully replicate on the hardest mathematical problems.

Where Claude Sonnet 4.6 Wins

**Writing quality.** Claude Sonnet 4.6 produces the most natural, well-structured prose of any frontier model. For content generation, customer-facing text, and applications where output reads like it was written by a skilled human, Claude remains the best choice.

**Instruction following.** On complex multi-constraint prompts, Claude Sonnet 4.6 satisfies more requirements more consistently. For agentic workflows with detailed system prompts, this reliability matters.

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Real-World Cost Comparison at Scale {#cost-comparison}

Monthly cost for 10 million input tokens and 5 million output tokens (a medium production workload):

| Model | Input Cost | Output Cost | Monthly Total | vs Gemini 3.1 Pro | | --- | --- | --- | --- | --- | | **Gemini 3.1 Pro** | **$20** | **$60** | **$80** | Baseline | | GPT-5.4 | $25 | $75 | $100 | +25% | | Claude Sonnet 4.6 | $30 | $75 | $105 | +31% | | DeepSeek V4 | $3 | $2.50 | $5.50 | -93% |

At 100 million input / 50 million output tokens per month (large production):

| Model | Input Cost | Output Cost | Monthly Total | vs Gemini 3.1 Pro | | --- | --- | --- | --- | --- | | **Gemini 3.1 Pro** | **$200** | **$600** | **$800** | Baseline | | GPT-5.4 | $250 | $750 | $1,000 | +25% | | Claude Sonnet 4.6 | $300 | $750 | $1,050 | +31% | | DeepSeek V4 | $30 | $25 | $55 | -93% |

The pattern is consistent: Gemini 3.1 Pro saves 25-31% versus other flagships at any scale. DeepSeek V4 is dramatically cheaper but operates in a different reliability and compliance tier.

For teams needing a mix of models, TokenMix.ai provides unified API access to all four, with automatic routing to optimize cost without sacrificing quality for each request type.

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Best Use Cases for Gemini 3.1 Pro {#use-cases}

**Research and scientific analysis.** GPQA Diamond 94.3% makes this the default choice for applications requiring expert-level reasoning over scientific literature, medical data, or technical documentation.

**Code generation and review.** SWE-bench 80.6% at $2/$12 is the best coding value at the flagship tier. For automated PR review, code generation pipelines, and bug resolution, the combination of quality and cost is unmatched.

**Multimodal applications.** Video understanding, image analysis, and audio processing in a single model. No other flagship handles all four modalities at this price point.

**High-volume production with quality requirements.** When you need flagship-tier quality but process enough tokens that a 25-31% cost difference translates to meaningful savings.

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Limitations and Trade-offs {#limitations}

**Writing quality trails Claude.** For content generation, marketing copy, and applications where prose quality is the primary metric, Claude Sonnet 4.6 remains superior. Gemini 3.1 Pro writes competent but less polished text.

**Ecosystem maturity.** Google's API ecosystem is smaller than OpenAI's. Fewer third-party integrations, smaller community knowledge base, and less battle-tested production deployment patterns.

**Availability in certain regions.** Google's AI services face restrictions in some markets. Verify availability for your deployment region before committing.

**Long-context pricing.** While the base rate is competitive, verify Google's surcharge thresholds for requests exceeding context limits. Pricing structures for very long context calls can differ from base rates.

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Decision Guide: When to Choose Gemini 3.1 Pro {#decision-guide}

| Your Situation | Choose Gemini 3.1 Pro? | Alternative | | --- | --- | --- | | Need best science/reasoning at flagship tier | Yes -- GPQA 94.3% is unmatched | N/A | | Need best coding value | Yes -- SWE-bench 80.6% at cheapest flagship price | DeepSeek V4 if reliability trade-offs acceptable | | Need best writing quality | No | Claude Sonnet 4.6 | | Need multimodal (video + audio + text) | Yes -- only flagship with all four modalities | N/A | | Need lowest possible cost | No | DeepSeek V4 ($0.30/$0.50) or Gemini Flash-Lite ($0.25) | | Need largest developer ecosystem | No | GPT-5.4 (OpenAI ecosystem) | | Need routing across multiple models | Use TokenMix.ai to combine all of the above | -- |

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Conclusion {#conclusion}

Gemini 3.1 Pro rewrites the value equation at the flagship tier. At $2/$12 per million tokens, it is 20-33% cheaper than GPT-5.4 and Claude Sonnet 4.6. At 94.3% GPQA Diamond and 80.6% SWE-bench, it matches or exceeds them on the benchmarks that matter most. This is not a budget model punching above its weight. This is the quality leader at the lowest flagship price.

The limitations are real -- Claude writes better prose, OpenAI has a bigger ecosystem. But for science, coding, multimodal, and any workload where cost-per-quality-unit matters, Gemini 3.1 Pro is the model to beat in April 2026.

Track the latest gemini 3.1 pro benchmark data and gemini api pricing changes on [TokenMix.ai](https://tokenmix.ai). The price war is ongoing, and every provider is releasing updates at unprecedented speed.

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*Author: TokenMix Research Lab | Updated: 2026-04-17*

*Data sources: Google DeepMind official documentation and pricing pages; GPQA Diamond and SWE-bench Verified leaderboards; OpenAI and Anthropic official pricing; benchmark comparison data tracked by [TokenMix.ai](https://tokenmix.ai). All figures verified as of April 2026.*

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FAQ {#faq}

Is Gemini 3.1 Pro really better than GPT-5.4 on benchmarks?

On GPQA Diamond (science reasoning), yes -- 94.3% vs approximately 88%. On SWE-bench Verified (coding), they are effectively tied -- 80.6% vs approximately 80%. GPT-5.4 has an edge on MMLU general knowledge. The overall picture: Gemini 3.1 Pro leads on science and matches on coding, while GPT-5.4 leads on general knowledge.

How much cheaper is Gemini 3.1 Pro than Claude Sonnet 4.6?

Gemini 3.1 Pro costs $2/$12 per million tokens (input/output). Claude Sonnet 4.6 costs $3/$15. That is 33% cheaper on input and 20% cheaper on output. At 50 million output tokens per month, the saving is $150,000 annually.

Should I switch from GPT-5.4 to Gemini 3.1 Pro?

If your primary workload is science reasoning, research, or multimodal processing, yes. If you depend heavily on OpenAI's ecosystem, tooling, or reasoning models (o3/o4-mini), the switching cost may outweigh the 20-25% price savings. The cleanest approach is to use both through a unified API gateway like TokenMix.ai and route by task type.

What is Gemini 3.1 Pro's biggest weakness?

Writing quality. Claude Sonnet 4.6 produces noticeably better prose for content generation, marketing copy, and customer-facing text. If output readability is your primary metric, Claude remains the stronger choice.

Can I use Gemini 3.1 Pro alongside other models?

Yes. Platforms like TokenMix.ai provide unified API access to Gemini 3.1 Pro, GPT-5.4, Claude Sonnet 4.6, and 150+ other models through a single endpoint. You can route requests by task type -- science queries to Gemini, writing tasks to Claude, budget tasks to DeepSeek -- without managing multiple API keys or SDKs.

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