Summary
Quick Decision (March 2026)
- Choose Opus 4.6 if: You need the deepest reasoning, strict instruction following, or work exclusively with text/code. It scores 80.8% on SWE-bench Verified and leads on multi-step reasoning consistency.
- Choose Gemini 3.1 Pro if: You need speed, cost efficiency, multimodal input, or native 1M context. It scores 80.6% on SWE-bench Verified at $1.25/$10 per million tokens, 4-7x cheaper than Opus.
- Use both via Morph: Route standard coding tasks to the cheaper model. Route complex multi-file reasoning to Opus. One API, optimal cost.
On the headline benchmark, these models are 0.2 percentage points apart. On price, they are 4-7x apart. The decision hinges on whether your workloads exploit Opus's reasoning advantages or whether benchmark-equivalent accuracy at a fraction of the cost is the smarter allocation.
Stat Comparison
Both models rated across the dimensions that determine which one earns its cost on a given workload.
Claude Opus 4.6
Reasoning depth and instruction following
"Highest reasoning depth. Premium pricing for premium accuracy."
Gemini 3.1 Pro
Speed, price, and multimodal leader
"Near-identical coding accuracy at a fraction of the cost."
Benchmark Deep Dive
The aggregate scores are nearly tied. The gaps emerge in specific task categories and difficulty tiers.
| Benchmark | Opus 4.6 | Gemini 3.1 Pro | What It Tests |
|---|---|---|---|
| SWE-bench Verified | 80.8% | 80.6% | Real GitHub issue resolution (500 tasks) |
| SWE-bench Pro | 55.4% | ~57% | Harder GitHub issues, cleaner dataset |
| HumanEval | 97.6% | ~97% | Function-level code generation |
| MATH 500 | 96.4% | ~93% | Competition-level math problems |
| MMLU-Pro | ~84% | ~85% | Multi-task language understanding |
| GPQA Diamond | 68.4% | ~66% | Graduate-level science questions |
SWE-bench: 0.2 Points
80.8% vs 80.6% is within measurement noise for a 500-task benchmark. Both models solve the same class of real-world GitHub issues. The difference is not statistically significant. On SWE-bench Pro, Gemini leads slightly at ~57% vs Opus at 55.4%. The two models trade leads depending on the benchmark variant.
Math and Reasoning: Opus Pulls Ahead
MATH 500: Opus at 96.4% vs Gemini at roughly 93%. GPQA Diamond: Opus at 68.4% vs Gemini at roughly 66%. These 2-3 point gaps on reasoning-heavy benchmarks reflect Opus's hidden thinking traces. The model spends more compute per token on internal reasoning, which pays off on problems requiring multi-step logical chains.
Language Understanding: Gemini Leads
On MMLU-Pro, Gemini 3.1 Pro slightly outperforms Opus. Google's training pipeline, built on the world's largest web corpus, gives Gemini an edge on broad knowledge retrieval. For tasks that depend on knowing facts rather than reasoning about code, Gemini has a slight advantage.
Opus 4.6 Profile
Leads on reasoning-heavy benchmarks: MATH 500 (96.4%), GPQA Diamond (68.4%). SWE-bench Pro at 55.4%. The thinking traces cost speed but buy accuracy on hard problems. Best for tasks where getting it right once saves retry cycles.
Gemini 3.1 Pro Profile
Matches Opus on SWE-bench Verified (80.6%), leads on MMLU-Pro, and runs 3-4x faster. Native 1M context at standard pricing. Best for high-volume workloads where cost and speed matter more than the last 2 points on reasoning benchmarks.
Pricing Comparison
The pricing gap is the headline difference. Both models score within 0.2% on the primary coding benchmark. One costs 4-7x less.
| Pricing Tier | Opus 4.6 | Gemini 3.1 Pro |
|---|---|---|
| Standard input | $5 / 1M tokens | $1.25 / 1M tokens |
| Standard output | $25 / 1M tokens | $10 / 1M tokens |
| Cached input | $0.50 / 1M tokens | $0.315 / 1M tokens |
| Batch API | 50% off standard | 50% off standard |
| 1M context pricing | $10/$37.50 (premium tier) | Standard rates |
Cost Per Equivalent Task
A typical coding task using 10,000 input tokens and 2,000 output tokens costs $0.10 on Opus and $0.03 on Gemini. At 10,000 API calls per day, that is $1,000/day vs $300/day. Over a month: $20,000 vs $6,000.
The Value Question
Paying 4x more for 0.2% higher accuracy on SWE-bench is not inherently wrong. The question is whether your specific tasks fall in the tail where Opus's reasoning advantage compounds. For standard implementation work, the numbers favor Gemini. For complex multi-step reasoning, Opus's hidden traces pay for themselves in fewer retries.
Context Window and Multimodal
Both models support 1M token contexts. The difference is accessibility and what else they can process beyond text.
| Capability | Opus 4.6 | Gemini 3.1 Pro |
|---|---|---|
| Default context | 200K tokens | 1M tokens |
| Extended context | 1M tokens (beta, premium pricing) | 1M tokens (standard pricing) |
| Image input | Yes | Yes |
| Audio input | No | Yes (native, up to 22 hours) |
| Video input | No | Yes (native, up to 2 hours) |
| Context caching | Prompt caching ($0.50/1M) | Context caching ($0.315/1M) |
Gemini's Multimodal Advantage
Gemini 3.1 Pro processes images, audio, and video natively. For coding tasks that involve UI screenshots, architecture diagrams, whiteboard photos, or recorded code walkthroughs, Gemini can work directly from the source material. Opus requires text descriptions or image-only input. If your workflow involves visual assets, this matters.
Context Window Access
Gemini offers 1M tokens at standard pricing. Opus charges premium rates ($10/$37.50 per 1M tokens) for requests exceeding 200K and it remains in beta. For large codebase analysis, whole-repo reasoning, or long document processing, Gemini's 1M context is both more accessible and cheaper.
When to Use Opus 4.6
Complex Multi-File Refactoring
Opus scores 55.4% on SWE-bench Pro, close to Gemini's ~57%. Opus leads SWE-bench Verified (80.8% vs 80.6%). On tasks requiring changes across many interdependent files, Opus's thinking traces catch cascading type errors and API contract violations that faster models skip.
Mathematical and Formal Reasoning
Opus at 96.4% on MATH 500 vs Gemini at ~93%. For algorithm design, complexity analysis, proof construction, or numerical correctness verification, Opus's extra reasoning compute produces measurably better results.
Strict Instruction Following
Opus follows complex multi-step prompts more deterministically. If your pipeline depends on exact output formats, specific coding conventions, or structured responses, Opus drifts less from the spec.
Anthropic Ecosystem Integration
If you already use Claude Code, Claude for Enterprise, or the Anthropic API with prompt caching tuned for Opus, switching to Gemini has migration cost. The 90% prompt caching discount ($0.50/1M) can close the price gap on repeated prompts.
When to Use Gemini 3.1 Pro
Cost-Sensitive High-Volume Workloads
At $1.25/$10 per million tokens, Gemini costs 4x less on input and 2.5x less on output than Opus. At 10,000 API calls/day, the savings exceed $14,000/month. For automated code review, batch processing, or CI/CD integration, the math is clear.
Multimodal Coding Tasks
Processing UI mockups, architecture diagrams, recorded walkthroughs, or visual bug reports alongside code. Gemini handles images, audio, and video natively. Opus handles images only. If your inputs are not pure text, Gemini is the only option.
Long-Context Analysis
Native 1M token context at standard pricing vs Opus's premium-priced beta. For analyzing entire codebases, processing long documents, or maintaining conversation history across extended sessions, Gemini is more practical.
Speed-Critical Applications
Gemini outputs 150-180 tok/s vs Opus at 46 tok/s. For interactive applications, real-time code suggestions, or latency-sensitive pipelines, Gemini is 3-4x faster. The time-to-first-token gap is even larger: Gemini responds in under 2 seconds, Opus averages 7.83 seconds.
How Morph Routes Between Them
When two models score within 0.2% on the primary benchmark but differ 4-7x on price, the optimal strategy is routing by task complexity.
Morph: Cross-Provider Model Routing
# Morph routes across providers automatically
# Standard coding task → cheapest model with sufficient accuracy
response = client.chat.completions.create(
model="morph-v3-fast",
messages=[{"role": "user", "content": "Add pagination to the /api/products endpoint"}]
)
# Complex reasoning task → highest accuracy model
response = client.chat.completions.create(
model="morph-v3-fast",
messages=[{"role": "user", "content": "Redesign the event sourcing system to handle 10x throughput"}]
)
# Morph selects the optimal model per task.
# Simple tasks get Gemini-tier pricing. Hard tasks get Opus-tier accuracy.Frequently Asked Questions
Is Opus 4.6 or Gemini 3.1 Pro better for coding?
They are nearly tied on SWE-bench Verified (80.8% vs 80.6%). Opus has a slight edge on harder reasoning tasks (SWE-bench Pro, MATH 500). Gemini is 3-4x faster and 4-7x cheaper. For most coding tasks, Gemini offers equivalent accuracy at a fraction of the cost. For complex multi-file reasoning, Opus's thinking traces provide a measurable edge.
How much cheaper is Gemini 3.1 Pro?
Gemini: $1.25/$10 per million tokens (input/output). Opus: $5/$25. Gemini is 4x cheaper on input, 2.5x cheaper on output. At 10,000 daily API calls, monthly savings exceed $14,000.
Which model has a larger context window?
Both support 1M tokens. Gemini offers it natively at standard pricing. Opus requires beta access and charges premium rates ($10/$37.50 per 1M) for requests exceeding 200K tokens. For cost-effective long-context work, Gemini wins.
Which model is better for multimodal tasks?
Gemini 3.1 Pro processes images, audio (up to 22 hours), and video (up to 2 hours) natively. Opus supports image input only. For any workflow involving visual or audio content, Gemini is the clear choice.
Which model is faster?
Gemini: 150-180 tok/s, under 2s TTFT. Opus: 46 tok/s, 7.83s average TTFT. Gemini is 3-4x faster on throughput and 4x faster to first token. For interactive use, the latency difference is immediately noticeable.
Can I use both models through one API?
Morph's API routes between Anthropic and Google models automatically. Simple tasks go to the cheapest sufficient model. Complex reasoning goes to the most accurate. One endpoint, cross-provider optimization.
Route Between Opus 4.6 and Gemini 3.1 Pro Automatically
Morph's API routes across providers. Standard tasks get Gemini-tier pricing. Complex reasoning gets Opus-tier accuracy. One endpoint, 4-7x cost savings on the bulk of your workload.