GPT-5.6 Sol is the stronger model. Gemini 3.5 Flash is the cheaper model. The current data does not support calling them tied, and the price gap is too large to ignore.
Older versions created a different story. GPT-5.4 and Gemini 3.1 Pro were often compared as near-peers under an earlier scoring scale. In July 2026, the relevant comparison is GPT-5.6 Sol against Gemini 3.5 Flash, with evidence status shown beside each result.
Quick comparison
| Metric | GPT-5.6 Sol | Gemini 3.5 Flash |
|---|---|---|
| Overall rank | #3 | #8 |
| Overall score | 79.3 Estimated | 75.02 Supported |
| Coding rank | #3 | #12 |
| Coding score | 74.08 Estimated | 65.4 Supported |
| Agentic rank | #3 | #57 |
| Agentic score | 75.49 Supported | 49.1 Supported |
| Input/output price | $5/$30 | $1.50/$9 |
| Context | 1M | 1M |
Supported and Estimated are confidence labels, not separate leaderboards. Sol's overall and coding positions are useful estimates with wider uncertainty. Its agentic result is Supported. Gemini's three rows are Supported, so the lower scores cannot be explained away as missing-data punishment.
Overall capability
Sol's 79.3 puts it third overall, behind Claude Mythos 5 and Claude Fable 5. Gemini 3.5 Flash sits eighth at 75.02. A 4.28-point gap is meaningful at the top of the board, but it is not the whole deployment decision.
Gemini costs about 30% as much per token. For one million input and 200,000 output tokens, Sol costs $11 and Gemini costs $3.30. A workload that Gemini completes correctly can save 70% before caching, batching, or volume discounts.
Coding
Sol ranks third at 74.08, behind Mythos and Fable. Gemini ranks twelfth at 65.4. That makes Sol the better starting point for repository engineering, code repair, and difficult multi-file work.
Sol's coding label is Estimated, so do not treat the exact 8.68-point margin as laboratory precision. Put both models on real issues. Measure accepted patches, tests passed without repair, regressions, wall-clock time, and cost per successful change.
Gemini can still win high-volume autocomplete, explanation, and lightweight transformation tasks. Those workloads may not need the same model that leads long-horizon software engineering.
Agentic work
This is the least ambiguous part. Sol scores 75.49 and ranks third with Supported evidence. Gemini scores 49.1 and ranks fifty-seventh, also Supported.
For browser use, terminal work, multi-tool orchestration, and autonomous loops, start with Sol. Measure intervention rate and recovery after failed calls. Use Gemini only when a direct evaluation shows the lower price survives the capability gap.
Context and multimodal work
Both models advertise a 1M context window. Advertised context is capacity, not retrieval accuracy. Test the length and document structure you actually use.
Gemini remains a natural candidate for image, document, and mixed-media workflows. BenchLM keeps modality as a use-case lens rather than charging text-only configurations for capabilities they do not claim. A multimodal recommendation should use direct evidence for the relevant input type, not infer vision quality from the overall text rank.
Price and routing
| Monthly workload | GPT-5.6 Sol | Gemini 3.5 Flash |
|---|---|---|
| 1M input + 200K output | $11.00 | $3.30 |
| 10M input + 2M output | $110.00 | $33.00 |
| 100M input + 20M output | $1,100.00 | $330.00 |
The best architecture may use both. Send classification, extraction, summaries, and cheap first passes to Gemini. Escalate difficult coding and agentic work to Sol. Route by measured failure risk rather than prompt length alone.
Which should you choose?
Use GPT-5.6 Sol for agentic work, difficult coding, and tasks where failure costs more than the model bill. Its strongest current claim is the Supported third-place agentic row.
Use Gemini 3.5 Flash for high-volume general work, multimodal candidates, and workloads where $1.50/$9 materially changes the economics. Its Supported overall score is strong even though it trails Sol.
Use both when the request mix has a cheap majority and an expensive tail. A small router plus an evaluation-backed escalation rule can capture most of Gemini's savings without asking it to do Sol's hardest work.
The current answer is not a tie. Sol leads capability; Gemini leads price. Your error budget decides which lead matters.
Reader questions
Frequently asked questions
01Is GPT-5 better than Gemini in 2026?
GPT-5.6 Sol ranks higher than Gemini 3.5 Flash overall, 79.3 Estimated to 75.02 Supported. Sol also leads coding, 74.08 Estimated to 65.4 Supported, and agentic work, 75.49 to 49.1, both Supported. Gemini is much cheaper at $1.50/$9 versus $5/$30.
02Which is cheaper, GPT-5 or Gemini?
Gemini 3.5 Flash costs $1.50 input and $9 output per million tokens. GPT-5.6 Sol costs $5/$30. Gemini is about 3.3x cheaper on both input and output.
03Is Gemini better than GPT-5 for coding?
Not on BenchLM's current coding surface. GPT-5.6 Sol ranks third at 74.08 with Estimated evidence, while Gemini 3.5 Flash ranks twelfth at 65.4 with Supported evidence. The evidence label makes Sol's exact margin less certain, not Gemini the leader.
04Which is better for AI agents?
GPT-5.6 Sol. It ranks third in agentic work at 75.49 with Supported evidence. Gemini 3.5 Flash scores 49.1. The gap is large enough that Gemini should not be the default for long autonomous workflows without direct proof on the task.
05Should I switch from GPT-5 to Gemini?
Switch or route work to Gemini when it passes your acceptance tests and the 3.3x lower token price matters. Keep Sol for tasks where its coding or agentic advantage reduces interventions, retries, or failure cost.
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