Claude Opus 5 vs GPT-5.6 vs Gemini 3.6 Flash: Which AI to Choose

In short:

• Comparing three flagship lines as of August 2026: Claude Opus 5 (Anthropic), GPT-5.6 as part of Sol/Terra/Luna (OpenAI), and Gemini 3.6 Flash (Google) — with a separate explanation of why Gemini 3.6 Flash should not be confused with the older Gemini 3.1 Pro.
• In a direct benchmark comparison, Opus 5 outperforms GPT-5.6 Sol in 9 out of 12 shared tests, including SWE-bench Pro (+14.6 p.p.) and ARC-AGI-3 (3.9 times).
• Sol counterattacks on Terminal-Bench 2.1, DeepSWE, and BrowseComp — tasks with long terminal agent chains.
• Gemini 3.6 Flash is not a contender for the top spot in intelligence (50 vs. 61 for Opus 5 on the Artificial Analysis Intelligence Index), but a model for speed and price: approximately 4 times faster than Opus 5 and 3.3 times cheaper per token.
• For Java and Spring Boot, there are no direct benchmarks for the language from any vendor, so the reference point is general coding benchmarks (SWE-bench Pro) and experience with large codebases.

Contents

• Which models are we comparing
• Comparison of architectures and approaches
• Programming
• Agent Tasks
• Long Context
• Tool Calling
• MCP
• Code Generation
• Code Review
• Reasoning
• Math
• Multimodal
• API and pricing
• Operating speed
• Which model is best for Java
• Which model is better for Spring Boot
• Which model works better with large projects
• Summary table
• Frequently asked questions
• Conclusion

Which models are we comparing

Before diving into the numbers, we need to distinguish the lines — otherwise, the comparison turns into a mess.

GPT-5.6 (OpenAI) is not a single model, but a family of three tiers, distilled from a single base training: Sol (flagship, `gpt-5.6-sol`), Terra (balanced, `gpt-5.6-terra`), and Luna (fast and cheap, `gpt-5.6-luna`). Public release was on July 9, 2026, after a limited preview starting June 26 for a trusted circle of partners. All three models have a context of ~1.05 million tokens and an output limit of 128 thousand tokens.

Gemini 3.6 Flash (Google) was released on July 21, 2026, and is the lower tier relative to Gemini 3.1 Pro, which still has Preview status (model `gemini-3.1-pro-preview`). But "lower tier" here doesn't mean "weaker" — according to Google, Flash outperforms Pro on most current coding and agentic benchmarks, and only falls behind on the most complex abstract reasoning tasks. The reason is simple: 3.1 Pro was trained until January 2025 and is stuck in pre-preview status, while 3.6 Flash has more recent data (up to March 2026) and Stable status. Therefore, in 2026, the comparison "Pro is better than Flash" no longer works automatically — it's worth checking specific benchmarks.

Claude Opus 5 (Anthropic) is the only model without internal sub-tiers by name, but with five effort levels (low → max), which effectively replace the idea of "model lines" with a single switch within one model. It was released on July 24, 2026, with a context of 1 million tokens (default and maximum simultaneously), and output up to 128 thousand tokens.

Comparison of architectures and approaches

Three companies have chosen three different strategies for balancing price and quality within a single model:

• OpenAI (tiered family): three separate models with different weights — Sol, Terra, Luna. The developer chooses the model for the task in advance; Terra is officially positioned as GPT-5.5 performance at approximately half the price of Sol, and Luna maintains full context at a quarter of Terra's cost.
• Anthropic (effort dial): one model, five levels of "effort" (low/medium/high/xhigh/max), which change the depth of thinking and, accordingly, the cost of the request on the fly — without switching between different models or API endpoints.
• Google (Flash strategy): focus on efficiency — fewer reasoning steps, fewer tool calls, and fewer output tokens for the same result, rather than increasing the number of "heavy" models. Gemini 3.6 Flash is clearly built to compete with more expensive flagships precisely through speed and token cost, not peak intelligence.

Practical implication: in GPT-5.6, choosing a model is a decision the developer makes once during integration. In Opus 5, effort is a request parameter that can be changed dynamically even within a single session. Gemini takes a third path — there is no explicit "effort" level switch here, efficiency is built into the model itself.

Programming

On the most demanding of common coding benchmarks — SWE-bench Pro (1865 real issues from active repositories like Django, Flask, React, Next.js) — Claude Opus 5 scores 79.2%, while GPT-5.6 Sol stops at 64.6%. The difference of 14.6 p.p. is the difference between a "senior" and a "middle" developer, if we compare it to how such performance gaps are usually described. Direct comparison with Gemini 3.6 Flash on SWE-bench Pro is absent in published data, but based on the overall Artificial Analysis Intelligence Index, the gap with Opus 5 is significant: 50 vs. 61 for Opus 5.

It's important to understand what SWE-bench Pro measures and why this gap is indicative. These are not tasks like "write a sorting function" — the model receives a real, open issue from a live repository, must independently find relevant files among hundreds of others, understand dependencies between modules, and generate a patch that passes the project's own test suite. This is much closer to a developer's daily work than isolated textbook tasks, and that's why a 14.6 p.p. gap here is more significant than the same gap would be, say, on HumanEval.

On SWE-bench Verified (a lighter, curated version of the same test), the gap between Opus 5 and Sol is already minimal — 96.0% vs. 95.0%, showing that for simple and medium tasks, all three vendors are very close to each other, and the divergence appears precisely on difficult issues.

This is a pattern I've seen since previous generations of models, and it's confirmed again here: the difference between flagships is almost always hidden not in simple tasks, but at the "tail" of complexity. If you evaluate a model only on easy examples — from demos, from short prompts in marketing materials — all three vendors look almost the same today. The difference becomes noticeable when a task requires keeping several files in mind simultaneously, understanding implicit dependencies, and not breaking existing tests. Therefore, my practical conclusion is this: if your team primarily writes isolated, well-specified code — the gain from switching specifically to Opus 5 will be negligible, and you should focus more on price and speed. But if you regularly encounter tasks like "fix a bug report in an unfamiliar part of a large legacy project" — this is where the 14.6 p.p. on SWE-bench Pro translates into real savings in review time and iteration count, and I would recommend testing Opus 5 first on such tasks, rather than simple CRUD examples where the difference between models is almost erased.

Agent Tasks

The picture here is uneven even within the Opus 5 / Sol pair:

• AutomationBench (end-to-end business automation): Opus 5 — 26.0%, Sol — 18.1%. Opus 5 has a significant advantage.
• OSWorld 2.0 (computer use): Opus 5 — 70.6%, Sol — 62.6%. Again, Opus 5 has the advantage.
• Terminal-Bench 2.1 (CLI agent work): Sol wins here — 91.9% in Ultra mode with parallel sub-agents vs. 89.1% for Opus 5.
• DeepSWE v1.1 (long engineering cycles): Sol is ahead again — 72.7% vs. 68.8%.
• BrowseComp (agent browsing): Sol has a minimal advantage — 92.2% vs. 90.8%.

For Gemini 3.6 Flash, there are no direct figures for these same tests in a comparable format, but Google reports its own improvement relative to 3.5 Flash: OSWorld-Verified increased from 78.4% to 83.0%, and token costs on long engineering tasks decreased to 65% due to a reduction in the number of steps.

I deliberately broke down these five benchmarks separately, rather than combining them into one average score — because in practice, "agent tasks" are not a single category, but at least two different in nature: BrowseComp and Terminal-Bench measure how well the model performs a long sequential chain of actions in one environment (browser, terminal), while AutomationBench and OSWorld 2.0 measure how well the model navigates a heterogeneous, less predictable environment and completes a business task without human intervention. These are different skills, and that's why Opus 5 and Sol swap places depending on which one is being tested. When I see such a "cross-shaped" distribution of advantages in benchmarks, it's a signal for me not to look for a single winner, but to see which specific skill is closer to your product.

From my own experience building agentic systems, I'll add another nuance that none of these benchmarks show: the result of AutomationBench or BrowseComp strongly depends not only on the model, but also on what specific tools the agent has at its disposal and how well their selection is organized. I analyzed this in detail using search tools for agents — Which Search APIs do developers choose and where do they make mistakes — and showed there why even the strongest model will start confusing tools if there are too many of them. That is, before concluding "Opus 5 is better for automation because of a higher score on AutomationBench," you should check if the bottleneck is the architecture of your tools, not the model itself.

Conclusion for the section: if your agent primarily works in the terminal and performs long multi-step CLI cycles — Sol has a real advantage due to parallel sub-agents. If the agent is more about computer use, end-to-end business process automation, or tool orchestration — Opus 5 has the advantage. But I would add a third point from myself: before choosing a model for these tasks, make sure that the reason for weak results on your side is the model itself, not the architecture of the tools around it.

Long Context

Formally, all three models claim approximately the same context window size — 1–1.05 million tokens. But the window size and the quality of information recall from it are different things. According to Google's internal testing, Gemini 3.6 Flash shows noticeably better long-term recall than the previous Gemini 3.5 Flash and outperforms Gemini 3.1 Pro on MRCR v2 (a "lost needle" test in long context). Comparable published MRCR figures for Claude Opus 5 and GPT-5.6 are not yet available, so a direct comparison of the three models by recall quality (not just window size) is not yet possible — this should be tested with your own data, not relied upon by the marketing window size.

The practical difference that is in the numbers: maximum output. For Opus 5 and GPT-5.6 — 128 thousand tokens per request, for Gemini 3.6 Flash — 64 thousand. For tasks where a large amount of code or documentation needs to be generated in a single call, this is a noticeable limitation specifically for Gemini.

Tool Calling

OpenAI has bet on programmatic tool calling: GPT-5.6 can write lightweight JavaScript code that coordinates multiple available tools, processes intermediate results, and passes data between calls within a single hosted runtime — instead of returning to the model between each individual action. According to OpenAI, this reduces the number of "round trips" and token consumption for workflows limited by complexity.

Anthropic has taken a different approach and added two beta API features to Opus 5: changing the toolset mid-conversation without invalidating the prompt cache, and automatic fallback transitions to another model for requests flagged by safety classifiers, instead of a complete refusal.

Google has historically been strong in reliable tool calling in the Flash line for their price: the previous Gemini 3.5 Flash led the MCP Atlas (83.6%), outperforming even the then-current Claude Opus 4.7 and GPT-5.5. Direct published MCP Atlas figures for Gemini 3.6 Flash in comparison with Opus 5 and Sol are not available at the time of writing.

Here I want to add an important caveat from my own experience, which none of these benchmarks show: all three approaches — programmatic calling, mid-conversation toolset changes, or simply high model accuracy — solve the problem of tool call *quality*, but none of them save from the opposite problem — the *quantity* of tools available to the model simultaneously. Even the best model on MCP Atlas will start confusing tools if there are 30 or 50 in the system prompt, not 3-5 — this is a documented effect that I analyzed in detail using Spring AI: Tool RAG — what to do when an agent has too many tools. According to the RAG-MCP study, which I reference there, selection accuracy drops from ~90% with 10 tools to a critical 13.62% with 100+ tools — and this happens regardless of whether GPT-5.6, Opus 5, or Gemini is working under the hood.

Practical conclusion: mid-conversation tool changes in Opus 5 is precisely the feature that directly helps to cope with this problem at the architecture level, not just at the model quality level. Instead of keeping the entire possible set of tools in context "just in case," the developer can dynamically load only those few that are relevant to the current step of the conversation — this is the same principle that underlies Tool RAG, only implemented at the API level, not at the developer's own infrastructure level. If your agent already has more than 15-20 tools, I would recommend looking specifically at this Opus 5 feature in conjunction with your own Tool RAG or routing layer, rather than hoping that a "smarter model" will figure out a large tool registry on its own.

MCP

Before moving on to the numbers — a brief explanation of the protocol itself, as some readers may encounter this abbreviation for the first time. MCP (Model Context Protocol) is an open standard introduced by Anthropic in November 2024 for unified connection of AI models to external data sources and tools: databases, file systems, APIs, internal company services. Before MCP, each developer wrote their own integration for each "model + tool" pair — connecting Claude to Google Drive looked different from connecting GPT to the same Google Drive. MCP solves this like a USB port for AI: one standard protocol through which any compatible model can "connect" to any compatible tool without writing custom code for each pair.

A simple example from my experience developing with Spring AI: if your agent has access to an MCP server that wraps the company's internal CRM, a request like "show all open deals for client Ivanov for the last quarter" is processed by the model as follows: it recognizes that data from the CRM is needed, contacts the corresponding MCP server via the standard protocol, receives a structured response, and forms the result. Without MCP, you would have to write a separate tool connector specifically for this CRM, specifically for this model, and repeat this work for each new "model — service" pair. Since MCP is an open standard, not Anthropic's proprietary technology, both OpenAI and Google support it today — so comparing models by their quality of work with MCP has practical significance, and is not an artificial advantage of Anthropic's "homegrown" technology.

This is one of the few sections where there is a direct three-way comparison in numbers — at least for the Opus 5 / Sol pair. On MCP Atlas (a benchmark for tool orchestration via Model Context Protocol), Claude Opus 5 scores 85.8%, GPT-5.6 Sol — 75.3%. Opus 5's advantage of 10.5 p.p. is one of the largest gaps in the entire comparison, and this is logical: Anthropic is the author and main driver of the MCP protocol itself, so deeper integration with it in its own model is not surprising.

For Gemini 3.6 Flash, a direct MCP Atlas result in comparison with Opus 5 and Sol has not been published, but given that the previous Flash model showed the best result among its generation competitors at the time, it is reasonable to assume that 3.6 Flash remains a strong choice for MCP-oriented tasks in terms of price/quality ratio — this is worth verifying with your own test before choosing for a production MCP pipeline.

My comment: if you are choosing a model specifically for an MCP-oriented product (an agent working with multiple internal systems via MCP servers), I would not make a conclusion based on a single benchmark. MCP Atlas measures orchestration quality in controlled conditions, but in production, the result depends just as much on how many MCP servers and tools are simultaneously connected to the agent — see the section above on Tool Calling and the problem of tool scale. A model with the highest MCP Atlas will still start confusing things if it has access to 10 MCP servers simultaneously without any filtering or routing layer.

Code Generation

At the level of "writing code from scratch based on a specification," the difference between models is less noticeable now than at the level of "fixing a real bug in someone else's repository" — this is evident from how closely Opus 5 and Sol performed on CursorBench 3.2 (67.7% vs. 67.2%, only a 0.5 p.p. difference) and SWE-bench Verified (96.0% vs. 95.0%). That is, for the typical task "generate a REST controller based on a description," all three models provide an acceptable result today — the main difference appears precisely on complex, multi-file, real-world tasks (SWE-bench Pro), where Opus 5 outperforms Sol by 14.6 p.p.

Code Review

Independent comparative data for code review simultaneously for all three models is not available at the time of writing — vendors primarily publish generation benchmarks, not benchmarks for reviewing someone else's code. What is known specifically about Claude Opus 5: an independent review by CodeRabbit identifies weaknesses precisely in error classes that are hardest to catch without a deep understanding of runtime behavior — logical errors, race conditions, and incorrect API usage. This does not mean that GPT-5.6 or Gemini 3.6 Flash are better in these specific categories — it's just that comparable data for them has not yet been published. Practical conclusion: for critical code review (production migrations, security, concurrent code), none of the three models should be used as the sole line of defense today.

Reasoning

The most indicative test for "pure" reasoning without relying on learned patterns is ARC-AGI-3. Here, the gap between Opus 5 and Sol is the largest in the entire comparison: 30.2% vs. 7.78% — almost 3.9 times. The ARC Prize Foundation called Sol's result a "historic milestone" (the first model to win the public ARC-AGI-3 game), but within two weeks, Anthropic had almost tripled this result.

On the generalized Artificial Analysis Intelligence Index, which combines nine different assessments (including GDPval-AA v2, GPQA Diamond, and Humanity's Last Exam), the ranking at the maximum "effort" level for each model looks like this: Opus 5 — 61 points, GPT-5.6 Sol — 59, Gemini 3.6 Flash — 50. This means that on the composite reasoning metric, Opus 5 leads, with a small gap from Sol and a noticeable gap from Gemini 3.6 Flash — which is logical, given Flash's positioning as a model for speed and price, not peak intelligence.

Math

A separate direct benchmark like AIME or MATH with published figures simultaneously for all three models could not be found at the time of writing — vendors are currently focusing more on agentic and coding benchmarks in their release materials. An indirect reference point is GPQA Diamond (graduate-level scientific reasoning), which is part of the Artificial Analysis Intelligence Index and influences the composite result described above. If mathematical calculations are a critical part of your use case, I would recommend not relying on the general Intelligence Index, but running your own set of tasks on all three models: the gap in pure mathematical reasoning may not coincide with the gap in the composite benchmark.

Multimodal

Here, Google's advantage is evident and consistent from generation to generation: Gemini was trained natively on image-text pairs from scratch, giving it a structural advantage specifically in video and multi-image tasks. Gemini 3.6 Flash accepts text, images, video, audio, and PDFs; according to independent tests (Roboflow Vision Evals), the model shows better results specifically on classic vision tasks.

Claude Opus 5 and GPT-5.6 are limited to text and images as input (without native video or audio), and based on practical observations, Claude shows strong results specifically on "applied" visual tasks for developers—code screenshots and technical diagrams—whereas GPT traditionally performs better on broad, general-purpose multimodal benchmarks. If your product actively works with video or has a lot of mixed content (photos + text in large quantities), Gemini 3.6 Flash is the safest choice among the three.

API and Pricing

The three different monetization models, already described in the architecture section, are now presented with specific figures:

With 100 million output tokens per month (a realistic volume for a production AI coding pipeline), Opus 5 is approximately $500 cheaper per month than Sol—despite winning on the vast majority of benchmarks. This is a rare combination of "better and cheaper simultaneously" instead of the usual compromise.

Gemini 3.6 Flash remains the cheapest option among the flagship models of the three vendors (not counting budget Luna or Flash-Lite): based on a composite blended calculation (7:2:1 cache/input/output), the cost is $1.16 per million tokens compared to $3.85 for Opus 5 at maximum effort—meaning it's 3.3 times cheaper.

Performance Speed (Fast mode Opus 5 vs Terra/Luna)

Speed is precisely the category where Gemini 3.6 Flash plays in its own league. On standard output, Gemini generates about 229.5 tokens per second—approximately 4 times faster than Opus 5 at maximum effort (53.7–59.8 tokens/sec). Time to first token (TTFT) is also telling: for Gemini 3.6 Flash, it's 15.26 seconds; for Opus 5 at maximum effort, it's 68.04 seconds (deep thinking costs time, a direct consequence of the effort dial architecture).

Anthropic's response to speed is through Fast mode: a 2.5x acceleration for double the price ($10/$50 per million tokens), available today without special deployment. GPT-5.6 does not have a separate "fast mode," but it does have the most impressive speed figure in this comparison: Sol deployed on Cerebras infrastructure achieves up to 750 tokens per second—approximately 12.5 times faster than Opus 5's standard output, albeit with limited availability of such deployment. Sol's standard output on OpenAI's API is 53.8 tokens/sec, which is roughly on par with Opus 5.

For the cheaper tiers of GPT-5.6 (Terra and Luna), there are no separate published speed figures in this comparison, but based on the distillation architecture from the same base model, it's logical to expect that the smaller Terra and especially Luna generate faster than Sol—this aligns with the general practice of "smaller model = lower latency."

Which Model is Best for Java

The honest answer: there is no separate public benchmark for "AI models on Java tasks" from any of the three vendors—all published coding benchmarks (SWE-bench Pro, SWE-bench Verified, CursorBench) mix languages and repositories (primarily Python/JavaScript/TypeScript ecosystems like Django, Flask, React, Next.js). Therefore, it's impossible to give a direct, honest answer like "model X is best for Java" based on published figures.

What can be stated reasonably: if your Java project involves working with real, complex, multi-file issues (rather than just generating code from scratch based on a specification), you should focus on SWE-bench Pro rather than CursorBench or SWE-bench Verified—and there, the advantage of Opus 5 over Sol is greatest (79.2% vs. 64.6%). For typical CRUD tasks, endpoints, and routine code, where all three models show similar results on lighter benchmarks, the difference between vendors will likely be less noticeable in practice—and then the choice should be made based on price and speed rather than expected code quality.

Which Model is Better for Spring Boot

Similar situation: there is no separate Spring Boot benchmark. However, Spring Boot projects in practice almost always involve tasks with "large context around simple code": configurations, dependencies between beans, annotations, service/repository/controller layers that need to be kept in mind simultaneously. This is more a matter of working with long context and multi-file changes than knowing the syntax of the language itself—and here, the Long Context and Agent Tasks sections above are relevant, rather than a separate "language" benchmark.

Considering this, I would orient myself as follows: for complex refactoring across multiple layers of a Spring application, a model with the best SWE-bench Pro and MCP Atlas result (i.e., Opus 5) appears to be a reasonable starting choice. For routine, well-isolated tasks (adding an endpoint, writing a DTO, generating a test), the difference between models will likely be non-critical, and it makes sense to focus on speed and cost.

Which Model Works Better with Large Projects

Here, it's worth distinguishing between two different scenarios that are often confused under the umbrella term "large project":

• A single request with a large context volume (analyze the entire repository, find all instances of deprecated API usage) — here, the main factor is not the window size (which is roughly the same for all three, ~1 million tokens), but the quality of recall from that window. Published data in favor of Gemini 3.6 Flash is stronger here than competitors — this is discussed above in the Long Context section — but there is no direct comparison with Opus 5 and Sol on the same test.
• A long autonomous chain of actions (an agent that independently goes through dozens of refactoring steps) — here, DeepSWE and Terminal-Bench 2.1 are relevant, where the advantage lies with Sol (72.7% and 91.9% compared to 68.8% and 89.1% for Opus 5). For tasks where the project is so large that the agent needs to perform many sequential CLI actions without human intervention, GPT-5.6 Sol has a documented advantage.

That is, "large project" by itself is not a specific enough formulation for choosing a model; one should understand whether it's about context size or the length of the autonomous chain of actions, and look at the corresponding benchmark above.

Summary Table

Frequently Asked Questions

How does Gemini 3.6 Flash differ from Gemini 3.1 Pro?

3.1 Pro is an older model in Preview status with training data up to January 2025, focused on the most difficult reasoning tasks. 3.6 Flash is newer, with more recent data (up to March 2026) and Stable status, which in practice outperforms 3.1 Pro on most current coding and agentic benchmarks at a significantly lower price.

Which model is the cheapest among the three?

Gemini 3.6 Flash is $1.50/$7.50 per million tokens. Among budget options, GPT-5.6 Luna is even cheaper ($1/$6), but this is no longer flagship level, but a separate fast and cheap tier.

Which model is the fastest?

On standard output, it's Gemini 3.6 Flash (~229 tokens/s). With a special deployment on Cerebras, GPT-5.6 Sol can reach 750 tokens/s, but this is limited availability, not a standard API option.

Is there a direct comparison of Claude Opus 5 and Gemini 3.6 Flash across all benchmarks?

No, no vendor publishes a complete set of common benchmarks for all three models simultaneously. The most complete direct comparison in numbers exists for the Opus 5 and GPT-5.6 Sol pair (12 common benchmarks); for Gemini 3.6 Flash, there are mainly individual metrics relative to previous Gemini versions and a composite Artificial Analysis Intelligence Index.

Conclusions

After analyzing the published benchmarks and the direct comparison of Opus 5 with Sol, I don't see a single winner for all cases – and that's why I consider the table above more useful than trying to name the "best model of 2026". To briefly summarize my own position: for working with real code in production repositories, MCP orchestration, and a better price/quality ratio, I would start with Claude Opus 5 – the numbers on SWE-bench Pro and MCP Atlas are compelling here, and at the same time, output tokens cost 17% less than with Sol.

For long terminal agent cycles and tasks where raw speed is critical with special deployment, I would test GPT-5.6 Sol – the advantage on Terminal-Bench 2.1 and DeepSWE is real, and access to Cerebras infrastructure provides speed unattainable by the other two models in this comparison.

I would consider Gemini 3.6 Flash not as a competitor for peak quality, but as a workhorse for high-volume, latency-sensitive, and budget-conscious scenarios, as well as any tasks involving video or a large number of images – here, Google's advantage is consistent from generation to generation. The main practical advice: do not blindly rely on any of the published benchmarks for your specific Java/Spring Boot task – no vendor publishes language-specific numbers, so a short in-house acceptance test on a real fragment of your code will provide more signal than any table in this article.