MModelspectraIndependent AI Model Intelligence
Head-to-head · Updated 2026-09-08
中文
Model Comparison · Head to Head

GPT-6 Astra vs DeepSeek-V4.1-Flash

GPT-6 Astra wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

VendorOpenAI / DeepSeek
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose GPT-6 Astra when coding depth matter most; choose DeepSeek-V4.1-Flash when lower cost, lower latency, fine-tuning/ecosystem is the priority.

Choose GPT-6 Astra if you…

  • Best-in-class computer use
  • Frontier research & reasoning
  • Autonomous long-horizon tasks
  • Strongest cybersecurity (gated)
  • Best for: Computer use, Deep research, Cybersecurity

Choose DeepSeek-V4.1-Flash if you…

  • 1M context at ~890 bytes/token global KV
  • Top agentic coding (DeepSWE 74.2, Terminal-Bench 2.1 90.6)
  • Persistent KV cut to ~1/8 via bounded replay
  • Open weights, extremely low serving cost
  • Best for: Long-context AI agents, Agentic coding, High-throughput deployment
02

Head-to-head aggregate scores

Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 27 tracked models.

GPT-6 Astra Higher overall
OpenAI · #1 overall
Overall96
Coding97
Multimodal90
VS
DeepSeek-V4.1-Flash
DeepSeek · #12 overall
Overall76
Coding89
Multimodal78

Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.

03

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.

DimensionGPT-6 AstraDeepSeek-V4.1-FlashVerdict
VendorOpenAI (US)DeepSeek (CN)Different vendors
Released2026.092026.09DeepSeek-V4.1-Flash is newer
Overall (rank)96 · #176 · #12GPT-6 Astra +20
Coding97 · #389 · #5GPT-6 Astra +8
Multimodal90 · #778 · #16GPT-6 Astra +12
Context window1.05M1MGPT-6 Astra larger
Max output128K128KTie
Effective-context9698DeepSeek-V4.1-Flash more reliable
Input $/1M$10$0.14DeepSeek-V4.1-Flash cheaper
Output $/1M$50$0.28DeepSeek-V4.1-Flash cheaper
Cache discount50% offnoneGPT-6 Astra deeper
Speed~35 tok/s~85 tok/sDeepSeek-V4.1-Flash faster
TTFT1.1s0.3sDeepSeek-V4.1-Flash snappier
Function calling9484GPT-6 Astra ahead
Refusal rate~13%~5%DeepSeek-V4.1-Flash less restrictive
English9984GPT-6 Astra
Chinese7286DeepSeek-V4.1-Flash
Modalitiestext, imagetext, imageSame
Open weightsNoYesDeepSeek-V4.1-Flash is open
Fine-tuningNoYes
Free tierNo free API tier; Fast mode delivers up to ~2x throughputDeepSeek App free; open weights to self-host
SOC2 / no-trainyes / yesno / yes
Private deploymentNoYesDeepSeek-V4.1-Flash

Fields drawn from vendor public documentation and the Modelspectra 27-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.

04

Dimension-by-dimension analysis

Reasoning & overall intelligence

GPT-6 Astra leads the overall aggregate by 20 points (96 vs 76). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while DeepSeek-V4.1-Flash remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a modest gap: GPT-6 Astra scores 97 against 89. On multi-file edits, SWE-style tickets and long-horizon agent loops GPT-6 Astra needs fewer correction turns; DeepSeek-V4.1-Flash is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

GPT-6 Astra leads multimodal 90 vs 78. Neither emits native video, so the comparison is about parsing images and documents, not generation.

Speed & latency

DeepSeek-V4.1-Flash is faster in interactive use: ~85 tok/s with 0.3s TTFT versus ~35 tok/s with 1.1s TTFT (about 2.4× 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 1.05M for GPT-6 Astra and 1M for DeepSeek-V4.1-Flash. Crucially, the larger nominal window does not win on usable recall: GPT-6 Astra advertises 1.05M but DeepSeek-V4.1-Flash scores higher on effective-context (98 vs 96), i.e. it actually retains more of what it was given.

Price & total cost

DeepSeek-V4.1-Flash is the cheaper API at $0.14/$0.28 versus GPT-6 Astra at $10/$50 per 1M input/output tokens — list input is about 71.4× lower. Cache discounts (GPT-6 Astra 50%) shift the effective bill, worked out below.

Chinese vs English

English: GPT-6 Astra 99 vs DeepSeek-V4.1-Flash 84. Chinese: 72 vs 86. For Chinese-language production, DeepSeek-V4.1-Flash is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.

Tool use & ecosystem

Function-calling score: GPT-6 Astra 94 vs DeepSeek-V4.1-Flash 84, so GPT-6 Astra has the edge on structured tool use. Fine-tuning is available from DeepSeek-V4.1-Flash. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.

05

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 monthGPT-6 AstraDeepSeek-V4.1-FlashGap
List priceno cache applied$2,500100M in × $10  +  30M out × $50$22100M in × $0.14  +  30M out × $0.28113.64×gap
With caching90% of inputs cache-hit$2,05090M cached in × $5  +  10M in × $10  +  30M out × $50$22no published cache discount93.18×gap

Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.

06

Decision tree

IF the workload is agentic or multi-file coding and a wrong first pass is expensive  →  choose GPT-6 Astra (coding 97 vs 89).
IF you serve real-time users and latency is a product KPI  →  choose DeepSeek-V4.1-Flash (~85 tok/s, 0.3s TTFT).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose DeepSeek-V4.1-Flash ($$0.14/$$0.28 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose DeepSeek-V4.1-Flash (effective context 98 vs 96).
07

Frequently asked questions

Is GPT-6 Astra worth the higher price over DeepSeek-V4.1-Flash?
At list the input rate is 71.4x higher, but cache discounts and output pricing narrow the effective gap. Pay the premium when GPT-6 Astra's stronger dimensions protect revenue; for routine volume DeepSeek-V4.1-Flash is the economical pick.
Does GPT-6 Astra's higher refusal rate matter in production?
GPT-6 Astra refuses about 13% of prompts versus 5% for DeepSeek-V4.1-Flash. In unattended pipelines that means more retries, fallbacks and manual review, raising effective cost and latency even when the token price is lower.
How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, GPT-6 Astra is about $2,050/month and DeepSeek-V4.1-Flash about $22/month after cache discounts ($2,500 and $22 at list).
Does the bigger context window actually matter?
Nominal windows are GPT-6 Astra (1.05M) and DeepSeek-V4.1-Flash (1M), but usable recall follows the effective-context score (96 vs 98). Prefer the higher effective-context model for long-document work where nothing can be missed.
How do they differ for Chinese-language and data-residency use?
DeepSeek-V4.1-Flash is the Chinese model (Chinese score 86, domestic cloud, possible private deployment) while GPT-6 Astra is the global model (Chinese 72, overseas API). Pick by language quality, access path and where data must reside.
Can I self-host either model?
DeepSeek-V4.1-Flash ships open weights and can be self-hosted (GPU permitting) for data control; GPT-6 Astra is a closed managed API with no self-hosting. Choose open weights when residency or cost-at-scale dominates, managed API for convenience.