DeepSeek V4-Pro is DeepSeek's best model for long-context work — it scores 90/100 vs 87/100 for DeepSeek V4-Flash, at $0.435/1M input tokens. Across all providers, GPT-6 Astra still leads long-context work at 100/100 — worth considering if you're not committed to DeepSeek.
The safest deepseek model for long context default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
DeepSeek V4-Pro is the strongest answer here for deepseek model for long context — pick it when quality of output matters more than the $0.43/1M/1M input you pay for it.
DeepSeek V4-Flash handles the same job for about 68% less per token. Start here and only move up if the output is not good enough.
DeepSeek V3 carries 128k tokens of context, so it is the pick for deepseek model for long context when whole documents, transcripts, or repositories go in at once.
DeepSeek V4-Pro leads DeepSeek's lineup for long-context work at 90/100 ($0.435/1M input, 1M context).
DeepSeek V4-Flash is the value pick at $0.14/1M input with a long-context work score of 87/100.
GPT-6 Astra (OpenAI) is the overall long-context work leader at 100/100 if provider choice is open.
Choose DeepSeek V4-Pro when long-context work quality is the priority and you're staying on DeepSeek.
Choose DeepSeek V4-Flash when token volume matters more than peak quality.
Teams open to other providers should also evaluate GPT-6 Astra before committing.
Switch the scoring lens to see whether the deepseek model for long context answer changes when cost, speed, or long-document depth leads the decision.
DeepSeek / Budget / Aug 6, 2026
Best open-weights flagship — near-frontier coding at a tenth of the price.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need vision input, verified agentic performance, or predictable pricing (surge pricing and an announced increase loom).
Where the deepseek model for long context recommendation shifts once you weigh price or latency differently.
Best open-weights flagship — near-frontier coding at a tenth of the price.
Best agentic capability per dollar in the directory.
GPT-4o-class coding quality at under $0.30/1M — the best value in the directory.
Open-source o1-class reasoning at a fraction of the cost.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| DeepSeek V4-ProDeepSeek | $0.43/1M | $0.87/1M | $6.09 | 1M tokens | Balanced | 93 | 80 | 85 |
| DeepSeek V4-FlashDeepSeek | $0.14/1M | $0.28/1M | $1.96 | 1M tokens | Fast | 87 | 74 | 78 |
| DeepSeek V3DeepSeek | $0.27/1M | $1.10/1M | $4.90 | 128k tokens | Fast | 87 | 74 | 80 |
| DeepSeek R1DeepSeek | $0.55/1M | $2.19/1M | $9.88 | 128k tokens | Deliberate | 84 | 60 | 89 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for deepseek model for long context, what it is genuinely good at, and where we would steer you away from it.
Our pick for deepseek model for long context. It scores 93/100 on the coding axis we weight this page by, and nothing else in this shortlist matches it on output quality.
DeepSeek's 1.6T-parameter (49B active) MoE flagship with hybrid sparse attention — near-frontier coding and reasoning at roughly a tenth of closed-rival pricing, MIT-licensed open weights.
You need vision input, verified agentic performance, or predictable pricing (surge pricing and an announced increase loom).
The open-weights frontier flagship of 2026. Self-reported numbers flatter it and independent agentic scores land lower, but even discounted it's the most capability per dollar in the directory's upper tier — with MIT-licensed weights.
Full pricing, benchmark table and release notes on the DeepSeek V4-Pro page.
The cost-conscious pick for deepseek model for long context, about 68% less per token than DeepSeek V4-Pro than the top choice while holding 87/100 on coding.
A 284B-parameter (13B active) MoE workhorse re-post-trained for agentic and coding tasks — beats the V4-Pro preview on every published agent benchmark at ultra-commodity pricing.
You need vision input or frontier-grade reasoning on the hardest tasks.
The best cheap agent engine of 2026. At $0.14/1M input with an 82.7 Terminal-Bench score, nothing touches its agentic capability per dollar. Use it for volume; escalate the hard 10% to a frontier model.
Full pricing, benchmark table and release notes on the DeepSeek V4-Flash page.
In this line-up because of context depth: the pick for deepseek model for long context when the input is too big to chunk.
GPT-4o-class coding quality at under $0.30/1M — the best value in the directory. Full DeepSeek V3 review →
The alternative to check next for deepseek model for long context — 84/100 on coding.
Open-source o1-class reasoning at a fraction of the cost. Full DeepSeek R1 review →
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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DeepSeek V4-Pro — it scores 90/100 on long-context work in this directory, ahead of DeepSeek V4-Flash at 87/100. Best open-weights flagship — near-frontier coding at a tenth of the price.
Not overall. GPT-6 Astra (OpenAI) leads the directory for long-context work at 100/100 vs DeepSeek V4-Pro's 90/100. DeepSeek V4-Pro is the best pick if you're staying within DeepSeek's ecosystem.
DeepSeek V4-Flash at $0.14/1M input tokens (long-context work score: 87/100). Use it for volume work and reserve DeepSeek V4-Pro for the tasks where quality matters most.
$0.435/1M input tokens and $0.87/1M output tokens via the API. Context window: 1M tokens. On a moderate month — 10M input and 2M output tokens — that works out to about $6.09, against $1.96 for DeepSeek V4-Flash.
Independent harnesses report much lower agentic scores than the self-reported numbers; trails GPT-5.6 and Opus-class on hard agentic evals. Peak-hour surge pricing doubles rates, a price increase is announced, and it's text-only (no vision). Concretely, avoid it if you need vision input, verified agentic performance, or predictable pricing (surge pricing and an announced increase loom). If none of that is negotiable, GPT-6 Astra (OpenAI) is the cross-provider leader at 100/100.
repository-level coding — 80.6% SWE-bench Verified (self-reported), the top open-weights score at release, competitive-programming-grade reasoning (Codeforces rating 3206), and self-hosted frontier capability under an MIT license. Its 1M-token context window is the practical limit on how much you can hand it in one go.
DeepSeek V4-Pro scores 90/100 on long-context work against 87/100 for DeepSeek V4-Flash, at 3x the input price. That premium is worth it on work where a wrong answer costs real time or money, and hard to justify on high-volume, low-stakes calls. Most teams run both and route by task rather than picking one.