大模型网摘deepseek-flash vs GPT-6.1 Terra: a 6x Price Gap and More Than Money
Put DeepSeek's and OpenAI's current workhorse tiers side by side and the price gap jumps out: deepseek-flash (DeepSeek-V4.1-Flash-1017) lists $0.15 in / $0.60 out per 1M tokens off-peak, while GPT-6.1 Terra — the tier that completed OpenAI's lineup in early November — runs $1/$5. Roughly 6.7x on input and 8.3x on output, enough to change the cost model of any scaled application. But price is never the whole story. Here are the two tiers on four axes, so you can decide by workload rather than by sticker.
Axis one: price structure
deepseek-flash's price list has three layers: cache-hit input as low as $0.003 per 1M tokens, cache-miss $0.15, doubling to $0.30 at peak (01:00–04:00 and 06:00–10:00 UTC, Mon–Fri); output $0.60 ($1.20 at peak). GPT-6.1 Terra's $1/$5 pricing shows no published peak/off-peak mechanism (details per OpenAI's official page). For workloads with heavy repeated prefixes — long system prompts in agents, batch document processing — DeepSeek's cache-hit rate pushes input cost close to negligible, an extra layer of savings on top of the headline gap. See our Terra pricing quick reference and the DeepSeek lineup rundown for details.
Axis two: context and output windows
deepseek-flash publishes a 1M-token context and 384K max output right in the spec sheet — a whole codebase or hundreds of pages fit in one call. We could not find a public figure putting Terra's context at the same order of magnitude; as of publication, defer to OpenAI's official docs. For long-document QA or repo-wide code audit, window size decides the architecture: if it fits, you skip retrieval chunking entirely.
Axis three: multimodal and computer use
Both accept images: flash has native vision (shipped with V4.1-Flash in September 2026), while Terra arrived positioned as the mid-tier with vision and computer use, completing OpenAI's Astra/Sol/Terra/Luna lineup (see our launch coverage). Computer use is Terra's differentiator — flash has no counterpart. If your scenario is "let the model drive the browser/desktop," that is the deciding vote.
Axis four: agentic coding
DeepSeek's docs list Claude Code, Codex, OpenCode and other agent tools as drop-in backends, with its own Harness coding agent in developer preview; the 1017 build was specifically tuned for agentic coding. The GPT-6.1 family Terra belongs to is likewise a fixture in mainstream coding tools. Ecosystem readiness is a wash on both sides — the separation shows up in real tasks, not leaderboards. Our earlier 1017 vs Kimi K3.1 Pro piece offers one hands-on framework that transfers directly to testing against Terra.
Bottom line: pick by workload, do the math by multiples
Three paths. High throughput, cost-sensitive, long context: deepseek-flash — off-peak plus caching lands near an order of magnitude below Terra's quote. Computer use needed, or deep OpenAI ecosystem ties: Terra — the premium buys capability surface. Unsure: run the same real tasks blind on both, then multiply by your monthly volume — a 6x price gap means products that pencil out on flash may not on Terra. Prices and specs above are compiled from official public pages; the latest official docs always govern.