LLM Model Scorecard: GPT-5, Claude 4, and Gemini 3 compared side-by-side

TL;DR

There is no single “best” frontier model in 2026. The right choice depends on workload: reasoning depth, cost tolerance, latency requirements, tool-use reliability, data governance needs, and multimodal support.

Based on current evidence (checked 2026-05-30):

The safe approach: shortlist two models from different providers, run a 50–100 case evaluation on your actual workload, and compare cost per completed task — not cost per token or leaderboard rank.

The frontier scorecard table

The table below compares GPT-5, Claude 4, and Gemini 3 across the dimensions that matter for procurement. Each row links to the site’s deeper article on that topic.

Model availability & context

Quality benchmarks (approximate standings, 2026-05-30)

Benchmark caution: These scores are approximate and date-stamped. Benchmark methodology, contamination and saturation affect all providers. A 2–3 point gap is rarely meaningful for procurement — treat these as shortlist filters, not rankings.

Pricing snapshot (USD per million tokens, 2026-05-30)

*OpenAI rates from third-party aggregator (Artificial Analysis) — OpenAI pricing pages blocked server-side access. Verify directly at platform.openai.com.

†Anthropic batch FAQ mentions cache pricing can combine with batch discounts but does not publish a simple batch row price.

The full pricing deep-dive with worked cost examples is at LLM API pricing comparison 2026.

Key pricing insight: Budget models (Gemini Flash, GPT-5 fast tier, Claude Haiku) are within 2–5× of each other for simple tasks. Premium models spread by 5–10×. Cache economics — not headline rates — often determine the real cost winner at scale.

Speed & latency characteristics

Modality & capability support

Multimodal note: All three providers can “see” images and extract information from them. Gemini is the only provider offering native audio and video input without a separate transcription or frame-extraction pipeline, which matters if your workload processes recorded meetings, live streams, or phone calls directly.

Data governance & portability

How to use this scorecard

This comparison table is designed as a starting point for your own evaluation. Do not pick a model from this page alone.

Step 1: Shortlist

Use the scorecard to narrow from all available models to 2–3 candidates that fit your workload’s primary constraint. For example:

• Cost-sensitive, high-volume classification: Shortlist GPT-5 mini, Gemini 2.0 Flash, Claude Haiku — budget models first.
• Complex reasoning with customer-facing output: Shortlist Claude Opus, GPT-5 premium — quality over cost.
• Multimodal processing (audio/video): Shortlist Gemini 2.5 Pro only, then verify against alternatives.
• High-reliability tool use (agent workflows): Shortlist GPT-5 and Claude Sonnet 4.5 — both strong on function calling.

Step 2: Evaluate

Build a 50–100 case test set from your actual workload. Include:

• Common cases that represent 80% of your traffic.
• Edge cases that have caused failures before.
• Known-answer questions to check factual grounding.
• Safety-relevant or policy-sensitive boundaries.

Run the same prompts through each shortlisted model. Score outputs against the same criteria. Compare cost per completed task, not cost per token.

The guide Creating a model scorecard for your own workload provides a repeatable framework for this step.

Step 3: Decide

Pick the model that passes your evaluation threshold at the lowest cost per completed task. Plan for:

• A fallback model from a different provider (for redundancy, not just quality).
• A re-evaluation schedule — every 90 days at minimum, more often if pricing or model versions change.
• An exit path — how would you switch if your chosen provider changes terms, pricing, or availability?

What this scorecard does not cover

• Self-hosted / open-weight models: Llama 4, DeepSeek V4, Qwen, Mistral and others are outside this comparison. See Hosted API vs self-hosted open model for that choice.
• Fine-tuned or custom models: Provider-specific fine-tuning options are not compared here. See Fine-tuning economics.
• Model gateways / routers: OpenRouter, LiteLLM and cloud AI platforms (Bedrock, Vertex, Azure) are separate decisions. See Model gateways compared.
• Small language models: Models under 7B parameters are not included. See Small language models.
• Regulatory compliance per jurisdiction: This comparison covers general data governance. For UK, EU or US-specific regulatory considerations, see LLMs in regulated industries.

When to check again

The ratings in this scorecard should be revisited when:

1. A provider releases a new flagship model or significant update.
2. Pricing changes by more than 20% on any model tier.
3. A new benchmark or evaluation methodology shifts the relative standings.
4. Your workload changes — new task types, new data sensitivity requirements, new volume projections.
5. It has been more than 90 days since the last check.

Methodology

• Data checked: 2026-05-30
• Sources consulted: Provider documentation, model cards, pricing pages, status pages and benchmark leaderboards from OpenAI, Anthropic and Google (Gemini). Third-party aggregators cited where direct access was blocked or unreliable.
• Directly verified at source (HTTP 200 from this server): Anthropic, Google, DeepSeek (where referenced)
• Blocked from server-side access: OpenAI pricing pages (HTTP 403) — rates estimated from third-party aggregator Artificial Analysis
• Benchmark scores: Approximate cluster positions, not precise ranks. Methodology noise, contamination and saturation make sub-5% differences unreliable for procurement.
• Assumptions: Pricing assumes standard pay-as-you-go API access without enterprise volume discounts or reserved capacity. Latency figures are provider-published or third-party measured ranges on paid tiers. Benchmark scores are drawn from the most recent published results as of the check date.
• Limitations: This scorecard provides a comparison snapshot, not live intelligence. Model versions, pricing and benchmark positions change frequently. It does not cover fine-tuned models, self-hosted inference, or provider-specific enterprise features. It is not legal, financial or procurement advice.
• Jurisdiction: Global. Data governance references are drawn from each provider’s public documentation. Local requirements (GDPR, CCPA, UK AI regulation) may impose additional constraints not fully covered here.

Source list

• OpenAI models overview — accessed 2026-05-30
• OpenAI API pricing — inaccessible from this server; cross-referenced via Artificial Analysis
• Anthropic Claude models — accessed 2026-05-30
• Anthropic pricing — accessed 2026-05-30
• Google Gemini API models — accessed 2026-05-30
• Google Gemini API pricing — accessed 2026-05-30
• Artificial Analysis model comparison — accessed 2026-05-30
• LMSYS Chatbot Arena — accessed 2026-05-30
• LiveBench — accessed 2026-05-30
• SWE-bench — accessed 2026-05-30
• OpenAI data controls FAQ — accessed 2026-05-30
• Anthropic privacy policy — accessed 2026-05-30
• Google Cloud data processing — accessed 2026-05-30

Related guides

• Creating a model scorecard for your own workload — the process guide for building your own evaluation
• LLM API pricing comparison 2026 — deep-dive on pricing with worked cost examples
• Benchmark leaderboards for busy buyers — how to read leaderboards without being misled
• OpenAI, Anthropic, Google and Mistral APIs: what comparison pages should measure — the rubric for comparing providers
• How LLM benchmarks work, and what they miss — understanding what benchmark scores actually measure
• Multimodal models explained — understanding modality differences across providers
• Provider data retention policies — data governance comparison
• Latency in LLM apps — understanding TTFT, throughput and user experience

Trust Stack

• Last checked: 2026-05-30
• Corrections: Contact us to report errors

Change log

• 2026-05-30: First draft. Side-by-side frontier model scorecard for GPT-5, Claude 4, and Gemini 3 covering benchmarks, pricing, context, modalities, speed and data governance.