Models

DeepSeek V3.1 Thinking vs Grok 4.5

Compare DeepSeek V3.1 Thinking from DeepSeek and Grok 4.5 from Grok on key metrics including benchmarks, price, context length, and other model features. Access both models and hundreds of others through the AIHubMix API.

DeepSeekDeepSeek V3.1 ThinkingGrokGrok 4.5
DeepSeek logo
DeepSeek V3.1 Thinking
DeepSeek · text → text

Thinking mode of DeepSeek-V3.1; DeepSeek V3.1 is a text generation model provided by DeepSeek, featuring a hybrid reasoning architecture that achieves an effective integration of thinking and non-thinking modes.

Input$0.56 /M
Output$1.68 /M
Grok logo
Grok 4.5
Grok · text, image → text

Grok 4.5 was trained on datasets spanning knowledge in coding, science, engineering, and math. With both intelligent and efficient reasoning, Grok 4.5 excels at real engineering tasks and exceeds comparable leading models at these tasks.

Input$2 /M
Output$6 /M
Cache read$0.5 /M

Pricing & Specifications

Prices are per million tokens. Time to First Token and throughput are rolling averages measured on AIHubMix.

DeepSeek V3.1 Thinking
Grok 4.5
Input /M
$0.56
$2
Output /M
$1.68
$6
Cache read /M
-
$0.5
Context length
128,000
500,000
Max output
32,000
500,000
Time to First Token
1.3 s
2.8 s
Throughput
31.5 tok/s
31.5 tok/s
Modalities
text
textimage
Supported Parameters
thinkingtoolsfunction callingstructured outputs
thinkingtoolsfunction callingstructured outputslong context
API Formats
Released
-
July 2026

Promotional prices show the discounted rate; see each model page for promotion windows.

Activity Past 30 Days

Daily traffic served through AIHubMix — how demand for each model is trending.

DeepSeek-V3.1-Thinkgrok-4.5

Tokens / day

-

Requests / day

-

Performance Past 3 Days

Measured on real AIHubMix traffic, hourly buckets. Gaps mean no traffic in that hour.

DeepSeek-V3.1-Thinkgrok-4.5

Throughput (tok/s)

-

TTFT (s)

-

Uptime (%)

-

LMArena Benchmarks

LMArena ratings by capability (Bradley-Terry, commonly called Elo). Higher is better.

Text
DeepSeek-V3.1-Thinkgrok-4.5
13801420146015001540
Overall
14171471
Coding
14581523
Math
14141479
Hard prompts
14371495
Instruction following
14191466
Multi-turn
14141476
Creative writing
14041451
Longer query
14461487
Chinese
14761512
English
14321476

Source: LMArena (arena.ai) leaderboard, imported by AIHubMix. Models without published ratings are omitted per chart.

Cost calculator

Estimate your monthly bill for the same workload on each model.

DeepSeek V3.1 Thinking
$58.80 /mo
Grok 4.5
$210 /mo

Monthly = daily × 30. Discounted rates applied where a promotion is active.

FAQ

Which is cheaper: DeepSeek V3.1 Thinking, Grok 4.5?

DeepSeek V3.1 Thinking: $1.68/M output tokens; Grok 4.5: $6/M. Use the cost calculator above to estimate your own workload.

How do their coding arena scores compare?

Grok 4.5: 1523; DeepSeek V3.1 Thinking: 1458 (LMArena coding leaderboard).

Which responds faster?

DeepSeek V3.1 Thinking: 1.3s time to first token measured on AIHubMix; see the live performance charts above for how each model behaves across the day.

How large is each context window?

DeepSeek V3.1 Thinking accepts 128,000 and Grok 4.5 accepts 500,000 input tokens. Maximum output per request is 32,000 tokens on DeepSeek V3.1 Thinking and 500,000 tokens on Grok 4.5.

Which one generates tokens faster?

DeepSeek V3.1 Thinking at 31.5 tok/s and Grok 4.5 at 31.5 tok/s, measured as output throughput on AIHubMix — a separate metric from time to first token.

What inputs and capabilities does each model support?

DeepSeek V3.1 Thinking accepts text input and supports thinking, tool calling, function calling and structured outputs; Grok 4.5 accepts text and image input and supports thinking, tool calling, function calling, structured outputs and long context.

Can I call DeepSeek V3.1 Thinking and Grok 4.5 with the same API key?

Yes. AIHubMix serves every model on this page behind one OpenAI-compatible endpoint, so switching between them is a one-line change to the model field — no second account, key or SDK.

Popular comparisons

Related model match-ups readers also look at.