GPT-5.4 vs Claude Opus 4.6 vs Gemini 3.1 Pro: Which Flagship AI Model Wins in 2026?
Last updated: March 6, 2026 · Pricing verified as of March 2026
Claude Opus 4.6 leads coding quality across current vendor-reported results, Gemini 3.1 Pro delivers 1M context at $2/1M input (source: ai.google.dev pricing), and GPT-5.4 is now listed on OpenRouter at $2.50/$20 with 1M context and 128K max output. If you need to pick a model today, Gemini 3.1 Pro is still the best value for most workloads, Opus 4.6 is still strongest for complex coding and agent tasks, and GPT-5.4 should be evaluated in parallel through routing because public benchmark coverage is still limited.
Here's the full breakdown.
TL;DR
- Gemini 3.1 Pro is the price-performance king: $2.00/$12.00 per 1M tokens with 1M context and 80.6% SWE-bench. Hard to beat for most production workloads.
- Claude Opus 4.6 wins on coding quality: 80.8% SWE-bench (single-attempt table) and 81.42% with prompt modification, 128K max output, and Agent Teams for multi-agent orchestration — but prompts over 200K tokens use premium pricing ($10/$37.50 per 1M).
- GPT-5.4 is now listed on OpenRouter: $2.50/$20 per 1M tokens, $0.625 cached input, 1M context, 128K max output. Independent benchmark coverage is still limited.
- For budget-sensitive teams: GPT-5.2 at $1.75/$14 per 1M tokens with 400K context and 80.0% SWE-bench is still a strong contender.
- Don't block delivery: Ship with Gemini 3.1 Pro or Claude Opus 4.6 now, and run GPT-5.4 side-by-side in your eval suite.
Quick Comparison Table
Every cell traced to a primary source. Pricing as of March 2026.
| Claude Opus 4.6 | Gemini 3.1 Pro | GPT-5.4 (OpenRouter) | GPT-5.2 | |
|---|---|---|---|---|
| Provider | Anthropic | Google DeepMind | OpenAI | OpenAI |
| Status | ✅ Available | ✅ Available | ✅ Available via OpenRouter | ✅ Available |
| Context | 200K (1M beta, tier 4/custom limits) | 1M | 1M | 400K |
| Max output | 128K tokens | 64K tokens | 128K tokens | 128K tokens |
| Input (/1M) | $5.00 (≤200K) / $10.00 (>200K) | $2.00 (≤200K) / $4.00 (>200K) | $2.50 (cached input: $0.625) | $1.75 |
| Output (/1M) | $25.00 (≤200K) / $37.50 (>200K) | $12.00 (≤200K) / $18.00 (>200K) | $20.00 | $14.00 |
| Reasoning | Extended thinking | Standard | Public mode naming still limited | Standard + deep thinking |
| SWE-bench | 80.8% (single) / 81.42% (prompt mod.) | 80.6% (single) | No widely accepted public number yet | 80.0% |
| Best for | Complex coding, agent orchestration | Long-context, multimodal, value | Early GPT-5.4 adoption and internal evals | Budget coding, general |
Sources: anthropic.com/pricing · anthropic.com/docs/models/claude-opus-4-6 · ai.google.dev pricing · deepmind.google model card · platform.openai.com/docs/models/gpt-5.2 · openrouter.ai/openai/gpt-5.4
When to Use Each Model
Pick Claude Opus 4.6 if you need the best code quality
In DeepMind's comparison table, Opus 4.6 is listed at 80.8% SWE-bench (single attempt). Anthropic separately reports up to 81.42% with prompt modification and notes 25-trial averaging in methodology (source: anthropic.com/news/claude-opus-4-6). The 128K max output is best-in-class — you can generate entire file diffs, full test suites, or multi-file refactors in a single response without truncation.
The Agent Teams feature is genuinely useful if you're building multi-agent systems. Opus can coordinate sub-agents, delegate tasks, and maintain context across orchestrated workflows.
The trade-off is cost. At ≤200K context, Opus is priced at $5/$25 per 1M tokens; above 200K, pricing moves to $10/$37.50. Relative to Gemini, the cost ratio is essentially unchanged across tiers (2.5× on input and ~2.08× on output).
Best scenarios: SWE-bench-style code repair, multi-agent pipelines, extended code generation (>64K output), safety-critical applications.
Pick Gemini 3.1 Pro if you want the best bang for your buck
Gemini 3.1 Pro offers a rare combination: 1M native context and genuinely competitive benchmarks at the lowest price point of any frontier model. At $2.00/$12.00 per 1M tokens (≤200K context), it's less than half the cost of Opus 4.6 while trailing by only 0.2 percentage points on SWE-bench.
Where Gemini really shines beyond coding:
- GPQA Diamond: 94.3% — PhD-level science reasoning (highest of all models)
- ARC-AGI-2: 77.1% — novel problem-solving
- HLE: 44.4% — high-level exam performance
- Terminal-Bench 2.0: 68.5% — terminal-based coding tasks
- Native multimodal: text + image + audio + video input in a single model
The 64K max output is the main limitation compared to Opus's 128K.
Best scenarios: Long document analysis (legal, medical), multimodal applications (video/audio processing), cost-sensitive production APIs, codebases that fit in 1M context.
Use GPT-5.4 now if... you want early production signals
Current public listing data (OpenRouter):
- 1M token context window
- 128K max output
- $2.50 / 1M input, $0.625 / 1M cached input, $20.00 / 1M output
What is still missing: broad, independent benchmark coverage across real production workloads.
Pragmatic approach: keep Gemini/Opus in production paths, route a controlled share of traffic to GPT-5.4, and promote only after your own evals pass.
Deep Dive: Context Window
| Model | Context Window | Notes |
|---|---|---|
| Gemini 3.1 Pro | 1M tokens | Production-ready 1M context |
| GPT-5.4 | 1M tokens | Listed on OpenRouter |
| GPT-5.2 | 400K tokens | Available now |
| Claude Opus 4.6 | 200K (1M beta) | 1M beta limited to tier 4/custom rate limits |
For teams working with large codebases, legal documents, or research corpora, Gemini 3.1 Pro's production-grade 1M context is a clear advantage over Opus 4.6's standard 200K. Opus's 1M beta exists, but Anthropic API docs gate it behind usage tier 4 or custom rate limits.
Deep Dive: Reasoning Capabilities
| Model | Reasoning Mode | Key Strength |
|---|---|---|
| Claude Opus 4.6 | Extended thinking | Multi-step debugging, architectural planning |
| Gemini 3.1 Pro | Standard (with thinking support) | GPQA Diamond 94.3%, ARC-AGI-2 77.1% |
| GPT-5.4 | Public mode naming still limited | Validate with your own eval suite |
Claude Opus 4.6's extended thinking is most effective for structured, multi-step reasoning: debugging complex code paths, analyzing system architectures, and working through long chains of dependencies.
Gemini 3.1 Pro's 94.3% GPQA Diamond score is genuinely impressive — this benchmark tests PhD-level science questions, and Gemini leads the field here. For research-oriented workloads, Gemini's reasoning breadth is a real advantage even without a branded "extended thinking" mode.
Deep Dive: Pricing & Cost Analysis
Cost Per Task (Estimated)
Based on typical token usage per task type. Prices at official rates.
| Task | Tokens (in/out) | GPT-5.2 | Gemini 3.1 Pro | Claude Opus 4.6 |
|---|---|---|---|---|
| Simple chat | 1K / 500 | $0.009 | $0.008 | $0.018 |
| Code review (single file) | 5K / 2K | $0.037 | $0.034 | $0.075 |
| Long doc analysis | 100K / 5K | $0.245 | $0.260 | $0.625 |
| Full codebase (200K+ ctx) | 300K / 10K | $0.665 | $1.380* | $3.375** |
*Gemini 3.1 Pro >200K context tier: $4.00/$18.00 per 1M tokens applies.
**Claude Opus 4.6 >200K premium long-context tier: $10.00/$37.50 per 1M tokens applies.
At high context utilization (>200K tokens), Gemini and Opus both move to higher pricing tiers, and their ratio remains roughly stable; the absolute dollar gap increases. The ranking shift is mostly versus GPT-5.2, where Gemini can become more expensive on very large-context tasks.
Through EvoLink (evolink.ai/models), you can access both Claude Opus 4.6 (from $4.13/1M input, -17%) and Gemini 3.1 Pro (from $1.60/1M input, -20%) at discounted rates via a unified OpenAI-compatible API.
Deep Dive: Coding Performance
| Model | SWE-bench | Conditions | Source |
|---|---|---|---|
| Claude Opus 4.6 | 80.8% (single) / 81.42% (prompt mod.) | Mixed sources | deepmind.google model card / anthropic.com/news/claude-opus-4-6 |
| Gemini 3.1 Pro | 80.6% (single) | Google evaluation | deepmind.google model card |
| GPT-5.2 | 80.0% | OpenAI evaluation | platform.openai.com |
| GPT-5.4 | No widely accepted public number yet | — | Available via OpenRouter |
Important caveat: SWE-bench scores from different vendors use different scaffolds and evaluation setups. The difference between 80.0%, 80.6%, and 80.8% is within the margin where test conditions matter more than model capability. Don't over-index on 0.2% differences.
What actually differentiates these models for coding in practice:
- Opus 4.6: 128K max output means it can generate entire multi-file patches without truncation.
- Gemini 3.1 Pro: 1M context lets you feed an entire codebase. Terminal-Bench 2.0 score of 68.5%.
- GPT-5.2: The cheapest option at $1.75/1M input with 80.0% SWE-bench. Good enough for most code review and generation tasks.
Decision Framework
Use this based on your primary constraint:
What About GPT-5.4? Should You Switch Now?
Short answer: don't hard-switch immediately; run a controlled rollout.
GPT-5.4 is now available through OpenRouter listing, but you should still validate quality, latency, and cost on your own workloads before full migration.
The pragmatic approach:
- Start building now with Gemini 3.1 Pro (best value) or Claude Opus 4.6 (best coding)
- Use an API gateway like EvoLink so you can switch models with a config change
- Evaluate GPT-5.4 immediately in your own benchmark suite
- Migrate if it wins — switching cost through a unified API is near zero
Also worth watching: DeepSeek V4 is in early access and could shake up the budget tier.
FAQ
Is GPT-5.4 better than Claude Opus 4.6?
It depends on your tasks, and there is still no broad independent benchmark consensus for GPT-5.4 yet. Claude Opus 4.6 is listed at 80.8% single-attempt in the DeepMind comparison table, and Anthropic reports up to 81.42% with prompt modification. Treat GPT-5.4 as a strong candidate to test, not an automatic replacement.
Which is cheaper: Claude Opus 4.6 or Gemini 3.1 Pro?
Gemini 3.1 Pro is significantly cheaper. At ≤200K context: $2.00/$12.00 vs Opus at $5.00/$25.00. At >200K context: $4.00/$18.00 vs Opus at $10.00/$37.50. The relative gap is nearly unchanged (2.5× input and ~2.08× output).
What is the context window of Gemini 3.1 Pro?
Gemini 3.1 Pro supports 1M tokens of context natively in production. This is the largest production context window among currently shipping frontier models.
Is GPT-5.4 available now?
GPT-5.4 is currently listed on OpenRouter with published token pricing and limits. Availability and billing details can still differ by provider and contract tier.
Can I use Claude Opus 4.6 with 1M context?
Claude Opus 4.6 has a standard 200K context window. A 1M beta is available but restricted to usage tier 4 or custom rate limits. For most developers, the practical limit is still 200K tokens.
Which model is best for coding?
On the single-attempt comparison table, Claude Opus 4.6 is 80.8%, followed by Gemini 3.1 Pro at 80.6% and GPT-5.2 at 80.0%. Anthropic also reports 81.42% for Opus with prompt modification. The differences are small — choose based on your budget and context window needs.
Is Gemini 3.1 Pro good for multimodal tasks?
Yes. Gemini 3.1 Pro is the only model in this comparison with native multimodal input supporting text, image, audio, and video in a single model. Claude and GPT support image input, but neither handles audio or video natively at the API level.
This page is updated as new information becomes available. Last checked: March 6, 2026.
Want to use GPT-5.4 now with model routing? Create a free EvoLink account (evolink.ai) and switch models through one unified API.
