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

DeepSeek-V4-Pro vs GPT-5.6 Terra

DeepSeek-V4-Pro 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.

VendorDeepSeek / OpenAI
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose DeepSeek-V4-Pro when coding depth, long-context reliability, lower refusal matter most; choose GPT-5.6 Terra when its stronger dimensions is the priority.

Choose DeepSeek-V4-Pro if you…

  • Strongest open source
  • Value champion
  • Excellent math reasoning
  • Extremely low price
  • Best for: Coding, Math reasoning, Open-source deployment

Choose GPT-5.6 Terra if you…

  • OpenAI quality
  • Halved price
  • Rich ecosystem
  • Best for: Value, General tasks, OpenAI ecosystem
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 22 tracked models.

DeepSeek-V4-Pro Higher overall
DeepSeek · #8 overall
Overall77
Coding79
Multimodal77
VS
GPT-5.6 Terra
OpenAI · #21 overall
Overall62
Coding70
Multimodal69

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.

DimensionDeepSeek-V4-ProGPT-5.6 TerraVerdict
VendorDeepSeek (CN)OpenAI (US)Different vendors
Released2026.042026.07GPT-5.6 Terra is newer
Overall (rank)77 · #862 · #21DeepSeek-V4-Pro +15
Coding79 · #570 · #12DeepSeek-V4-Pro +9
Multimodal77 · #1369 · #19DeepSeek-V4-Pro +8
Context window1M1.05MGPT-5.6 Terra larger
Max output128K128KTie
Effective-context9488DeepSeek-V4-Pro more reliable
Input $/1M$0.44$2.5DeepSeek-V4-Pro cheaper
Output $/1M$1.32$15DeepSeek-V4-Pro cheaper
Cache discountnone50% offGPT-5.6 Terra deeper
Speed~70 tok/s~60 tok/sDeepSeek-V4-Pro faster
TTFT0.5s0.5sTie
Function calling8290GPT-5.6 Terra ahead
Refusal rate~5%~13%DeepSeek-V4-Pro less restrictive
English8288GPT-5.6 Terra
Chinese8572DeepSeek-V4-Pro
Modalitiestexttext, imagedifferent coverage
Open weightsYesNoDeepSeek-V4-Pro is open
Fine-tuningYesNo
Free tierDeepSeek App free; new API users receive creditsNo free API
SOC2 / no-trainno / yesyes / yes
Private deploymentYesNoDeepSeek-V4-Pro

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.

04

Dimension-by-dimension analysis

Reasoning & overall intelligence

DeepSeek-V4-Pro leads the overall aggregate by 15 points (77 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: DeepSeek-V4-Pro scores 79 against 70. On multi-file edits, SWE-style tickets and long-horizon agent loops DeepSeek-V4-Pro needs fewer correction turns; GPT-5.6 Terra is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

DeepSeek-V4-Pro leads multimodal 77 vs 69. A concrete modality difference: GPT-5.6 Terra additionally handles image. Neither emits native video, so the comparison is about parsing images and documents, not generation.

Speed & latency

DeepSeek-V4-Pro is faster in interactive use: ~70 tok/s with 0.5s TTFT versus ~60 tok/s with 0.5s TTFT (about 1.2× 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 DeepSeek-V4-Pro 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 DeepSeek-V4-Pro scores higher on effective-context (94 vs 88), i.e. it actually retains more of what it was given.

Price & total cost

DeepSeek-V4-Pro is the cheaper API at $0.44/$1.32 versus GPT-5.6 Terra at $2.5/$15 per 1M input/output tokens — list input is about 5.7× lower. Cache discounts (GPT-5.6 Terra 50%) shift the effective bill, worked out below.

Chinese vs English

English: DeepSeek-V4-Pro 82 vs GPT-5.6 Terra 88. Chinese: 85 vs 72. For Chinese-language production, DeepSeek-V4-Pro is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.

Tool use & ecosystem

Function-calling score: DeepSeek-V4-Pro 82 vs GPT-5.6 Terra 90, so GPT-5.6 Terra has the edge on structured tool use. Fine-tuning is available from DeepSeek-V4-Pro. 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 monthDeepSeek-V4-ProGPT-5.6 TerraGap
List priceno cache applied$84100M in × $0.44  +  30M out × $1.32$700100M in × $2.5  +  30M out × $158.33×gap
With caching90% of inputs cache-hit$84no published cache discount$58890M cached in × $1.25  +  10M in × $2.5  +  30M out × $157.00×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-Pro (coding 79 vs 70).
IF you serve real-time users and latency is a product KPI  →  choose DeepSeek-V4-Pro (~70 tok/s, 0.5s TTFT).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose DeepSeek-V4-Pro ($$0.44/$$1.32 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose DeepSeek-V4-Pro (effective context 94 vs 88).
07

Frequently asked questions

Is GPT-5.6 Terra worth the higher price over DeepSeek-V4-Pro?
At list the input rate is 5.7x higher, but cache discounts and output pricing narrow the effective gap. Pay the premium when GPT-5.6 Terra's stronger dimensions protect revenue; for routine volume DeepSeek-V4-Pro is the economical pick.
Does GPT-5.6 Terra's higher refusal rate matter in production?
GPT-5.6 Terra refuses about 13% of prompts versus 5% for DeepSeek-V4-Pro. 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, DeepSeek-V4-Pro is about $84/month and GPT-5.6 Terra about $588/month after cache discounts ($84 and $700 at list).
Does the bigger context window actually matter?
Nominal windows are DeepSeek-V4-Pro (1M) and GPT-5.6 Terra (1.05M), but usable recall follows the effective-context score (94 vs 88). 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-Pro is the Chinese model (Chinese score 85, domestic cloud, possible private deployment) while GPT-5.6 Terra 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-Pro ships open weights and can be self-hosted (GPU permitting) for data control; GPT-5.6 Terra is a closed managed API with no self-hosting. Choose open weights when residency or cost-at-scale dominates, managed API for convenience.