| GPT-5.6 Sol, Claude Opus 5, and Gemini 3.1 Pro are the three highest-ranked large language models on independent benchmark trackers as of July 2026. Claude Opus 5 launched July 24, 2026, matching or beating its own flagship on coding benchmarks at half the price. GPT-5.6 Sol leads long, multi-step terminal tasks. Gemini 3.1 Pro leads cost-efficient reasoning at scale. No single model wins every category. The right choice depends on whether the task is reasoning, coding, or writing. |
GPT-5.6 Sol, Claude Opus 5, and Gemini 3.1 Pro are the three highest-ranked large language models on independent benchmark trackers as of July 2026, and each one leads a different category rather than one model beating the other two everywhere. Claude Opus 5 launched on July 24, 2026, Anthropic’s fourth model release in eight weeks. GPT-5.6 Sol leads agentic, terminal-based coding tasks. Gemini 3.1 Pro remains the most cost-efficient option for reasoning at scale. This comparison breaks down where each model wins on reasoning, coding, and writing, with dated pricing and benchmark figures attached to every claim.
Claude Opus 5, GPT-5.6 Sol, and Gemini 3.1 Pro: Release Dates That Reset This Comparison Every Few Weeks
Claude Opus 5 became Anthropic’s fourth model release in eight weeks when it launched on July 24, 2026, following Fable 5 and Mythos 5 in early June and Sonnet 5 at the end of that month. That release cadence is the reason any ranking in this category needs a date attached to it. A comparison written even three weeks earlier would already be measuring a different set of models.
GPT-5.6 Sol followed a similar path. OpenAI moved it from a limited preview on June 26, 2026, to wider API availability on July 9, 2026, as the flagship of a three-model family that also includes Terra, a balanced mid-tier option, and Luna, built for speed and lower cost.
Gemini 3.1 Pro has been generally available since February 2026, longer than either competitor’s current flagship. Google has previewed a faster Gemini 3.5 Flash successor, but 3.1 Pro remains the standard reference point for reasoning comparisons, since 3.5 Flash has not fully replaced it as the benchmark target most trackers use.
Release Timeline at a Glance
| Model | Release or Availability | Vendor |
| Claude Opus 5 | July 24, 2026 | Anthropic |
| GPT-5.6 Sol | Preview June 26, 2026; wider access July 9, 2026 | OpenAI |
| Gemini 3.1 Pro | Generally available since February 2026 | Google DeepMind |
GPT-5.6 Sol: Terminal-Bench 2.1 Score of 88.8% Leads Agentic Coding
GPT-5.6 Sol is OpenAI’s flagship model for agentic, multi-step, terminal-based work, scoring 88.8% on Terminal-Bench 2.1, a benchmark that measures how well a model completes long-running, multi-step terminal tasks without losing track of the goal. Sol’s Ultra configuration pushes that score to 91.9%, the highest reported result among the three models on this specific test.
On GPQA Diamond, a graduate-level science reasoning benchmark, GPT-5.6 Sol scores approximately 94%, putting it within range of Gemini 3.1 Pro on pure reasoning even though Sol is not primarily positioned as a reasoning-first model.
The model carries a 1.05 million token context window with a maximum output of 128,000 tokens, the largest context window among the three models compared here, though the practical difference at this scale is marginal. Standard pricing sits at $5 per million input tokens and $30 per million output tokens. Requests above 272,000 input tokens are billed at a higher rate for the entire request, a detail worth checking before running very long context jobs at scale.
OpenAI positions Sol for coding agents, computer use tasks, and workflows that coordinate several tools across a long session rather than a single short exchange. That agentic focus is where its Terminal-Bench 2.1 lead shows up most clearly, and it is the strongest reason to reach for Sol over the other two models when a task involves many sequential steps rather than one direct answer.
Claude Opus 5: Frontier-Bench Score Jumps to 43.3% at Half Its Flagship’s Price
Claude Opus 5 more than doubled its predecessor’s coding score on Frontier-Bench v0.1, reaching 43.3% against Opus 4.8’s 18.7%, on a 74-task benchmark that asks a model to turn engineering drawings into working software. Anthropic describes this as the largest single generation jump it has published on that specific test.
On CursorBench 3.2, a separate coding evaluation, Opus 5 scores within 0.5% of Anthropic’s own highest-scoring model, at roughly half the cost per task. That combination, near top-tier coding quality at a fraction of the price, is the core argument for Opus 5 as a daily driver rather than an occasional splurge.
Pricing has not changed from the previous Opus generation. Standard rates remain $5 per million input tokens and $25 per million output tokens, still the cheapest output price of the three models in this comparison. Context window sits at 1 million tokens with a 128,000 token maximum output, matching Gemini 3.1 Pro’s context size.
Anthropic also reports Opus 5 as its most aligned model to date on internal safety evaluation, with safety-related interventions triggering roughly 85% less often than with its prior flagship. For teams weighing reliability alongside raw capability, that drop in false positive safeguard triggers means fewer interrupted workflows during genuine coding and writing tasks.
Anthropic frames Opus 5 as the model to reach for daily, reserving its more expensive flagship tier for the narrower set of tasks that justify the higher cost and slower output. See Anthropic’s official Claude Opus 5 announcement for the full benchmark breakdown behind these figures.
Gemini 3.1 Pro: GPQA Diamond Score of Up to 94% at the Lowest Per-Token Price
Gemini 3.1 Pro remains the most cost-efficient frontier model for reasoning at scale, scoring roughly 92 to 94% on GPQA Diamond depending on which independent tracker is consulted, while pricing at $2 per million input tokens and $12 per million output tokens for prompts under 200,000 tokens.
That score range deserves an honest note. Independent benchmark trackers do not always agree on the exact figure for Gemini 3.1 Pro’s GPQA Diamond result, and this comparison reports the range rather than picking whichever single number looks most favourable.
Above the 200,000 token threshold, pricing rises to $4 per million input tokens and $18 per million output tokens, still well below both GPT-5.6 Sol and Claude Opus 5 at every pricing tier. The context window matches Opus 5 at 1 million tokens, and Gemini 3.1 Pro supports native multimodal input across text, image, audio, and video, a capability neither of the other two models matches at the same native level.
That combination of reasoning strength, native multimodal support, and the lowest per-token price makes Gemini 3.1 Pro the strongest starting point for high-volume reasoning workloads and multimodal document processing, where per-token cost compounds quickly once request volume climbs into the millions.
GPT-5.6 Sol vs Claude Opus 5 vs Gemini 3.1 Pro: Benchmark Scores and Per-Million-Token Pricing Compared
The three models diverge most clearly on price to performance rather than raw capability. Gemini 3.1 Pro costs roughly a third of GPT-5.6 Sol on output tokens while remaining competitive on reasoning benchmarks, and Claude Opus 5 undercuts GPT-5.6 Sol on output pricing while posting the largest single-generation coding score jump of the three.
Benchmark and Pricing Comparison
| Metric | GPT-5.6 Sol | Claude Opus 5 | Gemini 3.1 Pro |
| Context window | 1.05M tokens | 1M tokens | 1M tokens |
| Max output tokens | 128,000 | 128,000 | Not publicly standardized |
| Input price per million tokens | $5.00 | $5.00 | $2.00 (under 200K) |
| Output price per million tokens | $30.00 | $25.00 | $12.00 (under 200K) |
| GPQA Diamond (reasoning) | ~94% | Not directly comparable* | ~92-94% |
| Coding or agentic benchmark | 88.8% (Terminal-Bench 2.1) | 43.3% (Frontier-Bench v0.1) | Not directly comparable* |
Want to run these numbers against your own priorities? Universalnest’s AI Model Compare tool lets you re-rank all three models – plus DeepSeek V4 Pro – instantly by cost, coding benchmark, or context window.
*Anthropic and OpenAI publish different primary coding benchmarks, so these two scores do not measure the same test. Each figure is reported against its own disclosed benchmark rather than forced into a shared number that would misrepresent either model.
Best Model by Task: Gemini 3.1 Pro for Reasoning, GPT-5.6 Sol for Coding Agents, Claude Opus 5 for Writing
Gemini 3.1 Pro is the strongest starting point for reasoning-heavy and high-volume work, GPT-5.6 Sol is the strongest fit for long-running agentic coding tasks, and Claude Opus 5 is the strongest fit for daily coding and writing work that needs near frontier quality without frontier pricing.
For reasoning, Gemini 3.1 Pro’s GPQA Diamond score combined with its lower per-token cost makes it the default choice for high-volume reasoning and multimodal document work, particularly where thousands or millions of requests make token price the deciding factor over marginal benchmark gains.
For coding, the choice splits by task shape. GPT-5.6 Sol’s Terminal-Bench 2.1 lead suits long, multi-step agentic coding sessions where a model needs to hold context across many tool calls. Claude Opus 5’s Frontier-Bench jump and lower output price suit day-to-day coding work such as reviewing pull requests, writing tests, and fixing bugs inside a normal working session.
For writing, Anthropic’s Claude line has built a reputation across previous generations for more natural long-form tone control, and Opus 5’s pricing keeps that quality accessible for routine writing tasks without paying the rates of a flagship tier reserved for harder problems.
None of the three models wins every category, and the release cadence across 2026 means this ranking should be checked again in a few weeks rather than treated as settled. A model that leads one benchmark in July can lose that lead by the next release cycle, so matching the model to the task in front of you matters more than chasing whichever name currently tops a single leaderboard.
GPT-5.6 Sol, Claude Opus 5, and Gemini 3.1 Pro: the Foundation Layer Under Tools, Cybersecurity, and Regulation Coverage
These three reasoning and coding models sit underneath a wider layer of coverage across AI News and Tech: the everyday tools built directly on top of them, the cybersecurity risks that agentic capability introduces once a model can act across multiple steps without supervision, and the regulatory frameworks now governing how frontier models like these get released and monitored, including compliance deadlines under the EU AI Act. Understanding which model leads at reasoning, coding, or writing is the foundation for evaluating everything built on that capability, and future coverage in this category will connect these three models directly to the tools, security research, and policy decisions that depend on them.
Universalnest’s LLM Coverage: the Definition Guide and the AI News and Tech Pillar
These three models build directly on the token, context, and reasoning fundamentals covered in Universalnest’s introduction to large language models, which explains how context windows, token limits, and reasoning steps work before comparing specific models by name.
For the wider picture of where large language models fit alongside AI tools, cybersecurity, robotics, and regulation, see Universalnest’s AI News and Tech coverage, the reference point this comparison and every future model release in this category will link back to. Each new flagship release, whether from OpenAI, Anthropic, or Google, will be checked against the figures above and updated here rather than treated as a one-time snapshot.
Frequently Asked Questions
Is GPT-5.6 Sol available to every developer yet?
GPT-5.6 Sol launched as a limited preview on June 26, 2026, with wider API availability from July 9, 2026. Access has expanded steadily since, though OpenAI has not confirmed unrestricted general availability for every account tier as of this writing.
Does Claude Opus 5 replace Claude Fable 5?
No. Anthropic positions Opus 5 as the everyday premium model, matching or beating Fable 5 on several benchmarks at half the price, while Fable 5 remains the higher-ceiling option reserved for tasks that justify its higher cost and slower output.
Which of the three models has the largest context window?
GPT-5.6 Sol has the largest published context window at 1.05 million tokens. Claude Opus 5 and Gemini 3.1 Pro both use a 1 million token window. The difference is marginal enough that task fit matters more than raw window size.
Will Gemini 3.5 Flash replace Gemini 3.1 Pro for this kind of comparison?
Gemini 3.5 Flash has been positioned as a faster successor on select coding and agentic benchmarks, but Gemini 3.1 Pro remains Google’s standard flagship reference point for reasoning comparisons as of July 2026. This comparison will be revisited if that changes.
Does prompt caching change which model is actually cheapest?
Yes. All three providers support prompt caching, which can cut repeated context costs well below the standard listed rate. Actual cost depends heavily on how much context repeats across requests, so listed per-token pricing alone can be misleading at scale.
How often should a comparison like this be updated?
Given that Anthropic shipped four models in eight weeks and OpenAI moved GPT-5.6 Sol from preview to wider release within two weeks, comparisons in this category should be treated as accurate for only a few weeks before requiring a refresh.
