Gemini 3.5 Flash vs GPT-5.6 Terra
Gemini 3.5 Flash wins on Overall, Multimodal; GPT-5.6 Terra wins on Coding. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.
Verdict at a glance
Choose Gemini 3.5 Flash if you…
- Extremely fast
- Low price
- Native multimodal
- Built for scale
- Best for: Fast tasks, Multimodal, Low cost
Choose GPT-5.6 Terra if you…
- OpenAI quality
- Halved price
- Rich ecosystem
- Best for: Value, General tasks, OpenAI ecosystem
Head-to-head aggregate scores
Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.
Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.
Specs & pricing — every field side by side
List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.
| Dimension | Gemini 3.5 Flash | GPT-5.6 Terra | Verdict |
|---|---|---|---|
| Vendor | Google (US) | OpenAI (US) | Different vendors |
| Released | 2026.07 | 2026.07 | GPT-5.6 Terra is newer |
| Overall (rank) | 69 · #14 | 62 · #21 | Gemini 3.5 Flash +7 |
| Coding | 64 · #17 | 70 · #12 | GPT-5.6 Terra +6 |
| Multimodal | 86 · #7 | 69 · #19 | Gemini 3.5 Flash +17 |
| Context window | 1M | 1.05M | GPT-5.6 Terra larger |
| Max output | 128K | 128K | Tie |
| Effective-context | 88 | 88 | Tie |
| Input $/1M | $1.5 | $2.5 | Gemini 3.5 Flash cheaper |
| Output $/1M | $9 | $15 | Gemini 3.5 Flash cheaper |
| Cache discount | none | 50% off | GPT-5.6 Terra deeper |
| Speed | ~80 tok/s | ~60 tok/s | Gemini 3.5 Flash faster |
| TTFT | 0.3s | 0.5s | Gemini 3.5 Flash snappier |
| Function calling | 75 | 90 | GPT-5.6 Terra ahead |
| Refusal rate | ~10% | ~13% | Gemini 3.5 Flash less restrictive |
| English | 85 | 88 | GPT-5.6 Terra |
| Chinese | 72 | 72 | Tie |
| Modalities | text, image, audio, video | text, image | different coverage |
| Open weights | No | No | Both closed |
| Fine-tuning | Yes | No | |
| Free tier | Gemini App free; API free tier | No free API | |
| SOC2 / no-train | yes / yes | yes / yes | |
| Private deployment | No | No | Neither |
Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.
Dimension-by-dimension analysis
Reasoning & overall intelligence
Gemini 3.5 Flash leads the overall aggregate by 7 points (69 vs 62). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while GPT-5.6 Terra remains a strong generalist that is not out of its depth on routine work.
Agentic coding
This is a modest gap: GPT-5.6 Terra scores 70 against 64. On multi-file edits, SWE-style tickets and long-horizon agent loops GPT-5.6 Terra needs fewer correction turns; Gemini 3.5 Flash is still competent for scripts and assisted completion but trails as task complexity rises.
Multimodal
Gemini 3.5 Flash leads multimodal 86 vs 69. A concrete modality difference: Gemini 3.5 Flash additionally handles audio, video.
Speed & latency
Gemini 3.5 Flash is faster in interactive use: ~80 tok/s with 0.3s TTFT versus ~60 tok/s with 0.5s TTFT (about 1.3× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.
Context: window vs usable recall
Nominal windows are 1M for Gemini 3.5 Flash and 1.05M for GPT-5.6 Terra. Crucially, the larger nominal window does not win on usable recall: GPT-5.6 Terra advertises 1.05M but Gemini 3.5 Flash scores higher on effective-context (88 vs 88), i.e. it actually retains more of what it was given.
Price & total cost
Gemini 3.5 Flash is the cheaper API at $1.5/$9 versus GPT-5.6 Terra at $2.5/$15 per 1M input/output tokens — list input is about 1.7× lower. Cache discounts (GPT-5.6 Terra 50%) shift the effective bill, worked out below.
Chinese vs English
English: Gemini 3.5 Flash 85 vs GPT-5.6 Terra 88. Chinese: 72 vs 72. Both are US-based models; for Chinese-first workloads also compare domestic models on the leaderboard.
Tool use & ecosystem
Function-calling score: Gemini 3.5 Flash 75 vs GPT-5.6 Terra 90, so GPT-5.6 Terra has the edge on structured tool use. Fine-tuning is available from Gemini 3.5 Flash. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.
Cost worked example — same workload, real token math
Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.
| Scenario · per month | Gemini 3.5 Flash | GPT-5.6 Terra | Gap |
|---|---|---|---|
| List priceno cache applied | $420100M in × $1.5 + 30M out × $9 | $700100M in × $2.5 + 30M out × $15 | 1.67×gap |
| With caching90% of inputs cache-hit | $420no published cache discount | $58890M cached in × $1.25 + 10M in × $2.5 + 30M out × $15 | 1.40×gap |
Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.