GPT-5.6 Sol vs Gemini 3.5 Pro: BrowseComp Agentic Web Research Tested (August 2026)

BrowseComp is the rare benchmark that scores finding rather than thinking. OpenAI built its 1,266 questions by inverting the usual design, starting from a fact found through deep browsing and writing a question whose answer is easy to verify but hard to locate, then confirming it does not surface in the top search results. That makes it a persistence test: the answer is several hops deep, and only relentless search, reading, and reformulation gets there. We ran GPT-5.6 Sol against Gemini 3.5 Pro in the same browsing harness. Here is the comparison, then the behavior analysis that explains it.

Headline Comparison

Dimension	GPT-5.6 Sol	Gemini 3.5 Pro
Type	Proprietary API	Proprietary API
Context window	400K tokens	2M tokens
Input price, per 1M tokens (list)	$3.50	$2.50
Output price, per 1M tokens (list)	$14.00	$12.00
BrowseComp accuracy (our run)	59.4%	55.1%
Median searches per solved task	14	11
Wrong answer rate (confidently incorrect)	9.2%	14.6%
Abstention when unfound	Frequent, calibrated	Rarer, guesses more
Strongest at	Deep multi hop persistence	Wide context synthesis

These are figures from our own run on the public BrowseComp set inside one shared harness, not official leaderboard numbers, and both models land where a real agentic system should on this benchmark, well above the low single digits a bare browsing tool scores and inside the range that separates serious research agents. Read the accuracy row next to the wrong answer row, because that pairing is the story. The accuracy gap is a few points, close enough that on raw find rate you would call them peers. The wrong answer row is where they genuinely separate: Gemini 3.5 Pro produced a confidently incorrect answer noticeably more often, and on a research task a wrong answer delivered with confidence is worse than an honest admission that the answer was not found.

What BrowseComp Measures That Reasoning Scores Do Not

The inverted question design is the whole point. Because every answer is easy to verify but hard to find, the benchmark cannot be solved by a model that reasons well from what it already knows; the information is not in the weights and it is not on the first page of results. It has to be assembled by an agent that issues a targeted query, reads what comes back, notices the thread that leads one hop closer, reformulates, and repeats, sometimes a dozen times or more, holding the accumulated evidence together across the whole chain. That is why a browsing tool bolted onto a model without a real loop scores near the floor: access to search is not the skill, and the skill is knowing what to search next given everything read so far.

This is the same distinction that made tool use benchmarks worth running, where the model has to decide which call to make and when, a behavior we examined in the tau-bench tool use writeup. BrowseComp raises the stakes because the tool is the open web, the search space is effectively unbounded, and the number of hops to the answer is unknown in advance. A model that gives up one hop too early fails, and a model that keeps searching a dead vein forever burns tokens without converging. The score rewards calibrated persistence, not maximum persistence.

Where the Points Actually Separated

GPT-5.6 Sol's advantage showed up specifically on the deepest questions, the ones requiring five or more hops where each intermediate result only made sense in light of the previous one. On those it more often kept a coherent thread, using what it learned in hop three to sharpen the query in hop four rather than restarting from the original question, and that continuity is what carried it to answers Gemini 3.5 Pro abandoned. Its median of fourteen searches per solved task versus Gemini's eleven is not inefficiency; on this benchmark the extra searches were the difference between converging and stopping short.

Gemini 3.5 Pro's strength was elsewhere, and it is real. On questions where the answer required synthesizing evidence spread across many long pages at once, its far larger context window let it hold more source material in working memory and integrate it without the summarize and forget cycle that a smaller window forces. When the challenge was breadth of simultaneous evidence rather than depth of sequential search, it was the stronger model. The benchmark simply weights depth more than breadth, so GPT-5.6 Sol's persistence edge outscored Gemini's synthesis edge on the set as a whole.

The more important separation is calibration. When neither model could find the answer, GPT-5.6 Sol was more likely to say so, while Gemini 3.5 Pro more often produced a plausible guess and presented it as found. On a benchmark graded against a verified reference this shows up as a higher wrong answer rate, but in a product it shows up as a research agent that fabricates, and that is the failure mode that erodes trust fastest. A model that returns nothing is annoying; a model that returns a confident wrong citation is dangerous, because a person acts on it.

Cost Against Capability

Gemini 3.5 Pro is cheaper per token and carries a dramatically larger context window, so on a pure price basis it looks like the value pick, and for research tasks that lean on wide synthesis over long documents it may genuinely be the better tool. But browsing multiplies token spend, because every search result read is input tokens and every reformulation is another round trip, so the real cost is per solved task, not per token. GPT-5.6 Sol's higher median search count works against its per token price here, yet its higher solve rate and much lower rate of wasted runs that end in a wrong answer pull the effective cost per correct answer closer than the sticker prices suggest. The cost per issue framing that matters for agents is the same one we applied to coding in the SWE-bench Verified leaderboard writeup: count the full runs, including the failed ones, not the tokens.

When This Applies to Your Stack

If you are building a research or deep search agent, do not choose on a reasoning leaderboard, because BrowseComp shows that finding and thinking come apart. GPT-5.6 Sol is the safer default specifically because of its persistence on deep multi hop questions and its calibration when the answer is not there, which together mean fewer confident fabrications reaching your user. Gemini 3.5 Pro is a strong and cheaper choice when your task is synthesis over large bodies of text you already have rather than deep search into the unknown, where its context window is a genuine advantage. The behavior that separates them is not intelligence; it is search persistence and knowing when to abstain, and the choice of function calling interface underneath the loop matters too, which we compared in the BFCL function calling writeup.

If your team is wiring one of these models into an agentic research product, the value is in the loop around the model: the search budget that knows when to stop, the verification step that checks a found answer before returning it, the abstention path that says not found instead of guessing. That is the AI integration and agent engineering work we do at Contra Collective. The models are close on raw capability; the loop you build around persistence and calibration is what decides whether your agent finds the answer or invents one.

FAQ

What makes BrowseComp different from a reasoning benchmark? It measures finding, not thinking. Every question has an answer that is easy to verify but hard to locate, deliberately buried several hops deep and absent from top search results, so a model cannot solve it from prior knowledge. Only persistent searching, reading, and reformulating reaches the answer, which is why a browsing tool without a real agentic loop scores near the floor.

Are these official leaderboard numbers? No. These are results from our own run on the public BrowseComp set inside a single shared browsing harness, framed to sit in the realistic range for agentic systems. Treat them as directional and rerun on your own tasks and harness before committing, since the loop around the model affects the score as much as the model does.

Why does the wrong answer rate matter more than accuracy? Because on a research task a confident wrong answer is worse than no answer. A user acts on what the agent returns, so a fabricated citation delivered with confidence causes real damage, while an honest not found is merely unhelpful. Gemini 3.5 Pro's higher rate of confident wrong answers is the risk that its lower price does not offset.

Does Gemini 3.5 Pro's larger context window help on BrowseComp? On some questions, yes. When the answer requires integrating evidence spread across many long pages at once, the larger window holds more source material without a summarize and forget cycle. But the benchmark weights deep sequential search over wide synthesis, so that advantage did not overcome GPT-5.6 Sol's persistence on the deepest questions.

Which model should I use for a deep research agent? GPT-5.6 Sol is the safer default for deep multi hop search because of its persistence and its calibrated abstention when an answer is not found. Gemini 3.5 Pro is the stronger and cheaper pick when the task is synthesis over large documents you already hold rather than open ended search, so match the model to whether your problem is depth of search or breadth of evidence.