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

Claude Opus 4.8 vs DeepSeek-V4.1-Flash

Claude Opus 4.8 wins on Overall, Multimodal; DeepSeek-V4.1-Flash wins on Coding. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

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

Verdict at a glance

Bottom line: Choose Claude Opus 4.8 when its stronger dimensions matter most; choose DeepSeek-V4.1-Flash when lower cost, lower latency, fine-tuning/ecosystem is the priority.

Choose Claude Opus 4.8 if you…

  • Previous flagship still capable
  • Well-proven stability
  • Best for: Deep reasoning, Long documents, Coding

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.

Claude Opus 4.8 Higher overall
Anthropic · #7 overall
Overall81
Coding87
Multimodal89
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.

DimensionClaude Opus 4.8DeepSeek-V4.1-FlashVerdict
VendorAnthropic (US)DeepSeek (CN)Different vendors
Released2026.052026.09DeepSeek-V4.1-Flash is newer
Overall (rank)81 · #776 · #12Claude Opus 4.8 +5
Coding87 · #889 · #5DeepSeek-V4.1-Flash +2
Multimodal89 · #878 · #16Claude Opus 4.8 +11
Context window1M1MTie
Max output128K128KTie
Effective-context9698DeepSeek-V4.1-Flash more reliable
Input $/1M$5$0.14DeepSeek-V4.1-Flash cheaper
Output $/1M$25$0.28DeepSeek-V4.1-Flash cheaper
Cache discount90% offnoneClaude Opus 4.8 deeper
Speed~40 tok/s~85 tok/sDeepSeek-V4.1-Flash faster
TTFT1.0s0.3sDeepSeek-V4.1-Flash snappier
Function calling8884Claude Opus 4.8 ahead
Refusal rate~9%~5%DeepSeek-V4.1-Flash less restrictive
English9484Claude Opus 4.8
Chinese7886DeepSeek-V4.1-Flash
Modalitiestext, imagetext, imageSame
Open weightsNoYesDeepSeek-V4.1-Flash is open
Fine-tuningNoYes
Free tierNo free tierDeepSeek 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

Claude Opus 4.8 leads the overall aggregate by 5 points (81 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: DeepSeek-V4.1-Flash scores 89 against 87. On multi-file edits, SWE-style tickets and long-horizon agent loops DeepSeek-V4.1-Flash needs fewer correction turns; Claude Opus 4.8 is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Claude Opus 4.8 leads multimodal 89 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 ~40 tok/s with 1.0s TTFT (about 2.1× 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 Claude Opus 4.8 and 1M for DeepSeek-V4.1-Flash. Crucially, the larger nominal window does not win on usable recall: Claude Opus 4.8 advertises 1M 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 Claude Opus 4.8 at $5/$25 per 1M input/output tokens — list input is about 35.7× lower. Cache discounts (Claude Opus 4.8 90%) shift the effective bill, worked out below.

Chinese vs English

English: Claude Opus 4.8 94 vs DeepSeek-V4.1-Flash 84. Chinese: 78 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: Claude Opus 4.8 88 vs DeepSeek-V4.1-Flash 84, so Claude Opus 4.8 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 monthClaude Opus 4.8DeepSeek-V4.1-FlashGap
List priceno cache applied$1,250100M in × $5  +  30M out × $25$22100M in × $0.14  +  30M out × $0.2856.82×gap
With caching90% of inputs cache-hit$84590M cached in × $0.5  +  10M in × $5  +  30M out × $25$22no published cache discount38.41×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 DeepSeek-V4.1-Flash (coding 89 vs 87).
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 Claude Opus 4.8 worth the higher price over DeepSeek-V4.1-Flash?
At list the input rate is 35.7x higher, but cache discounts and output pricing narrow the effective gap. Pay the premium when Claude Opus 4.8's stronger dimensions protect revenue; for routine volume DeepSeek-V4.1-Flash is the economical pick.
How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, Claude Opus 4.8 is about $845/month and DeepSeek-V4.1-Flash about $22/month after cache discounts ($1,250 and $22 at list).
Does the bigger context window actually matter?
Nominal windows are Claude Opus 4.8 (1M) 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 Claude Opus 4.8 is the global model (Chinese 78, 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; Claude Opus 4.8 is a closed managed API with no self-hosting. Choose open weights when residency or cost-at-scale dominates, managed API for convenience.
Which one supports fine-tuning?
DeepSeek-V4.1-Flash supports fine-tuning; Claude Opus 4.8 does not at this tier. If you plan to adapt the model to a narrow domain, that is a concrete differentiator.