LLM alternatives

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Competitor → Thalam alternative

For every model, we map to the closest in-class equivalent.

UsingOpenAI

GPT-4o

Recommended→

DeepSeek V4 Pro

DeepSeek

Open-weight flagship with 1M context, native reasoning mode, and strong agentic coding.

Input / 1M
$1.69 / 1M
Output / 1M
$3.38 / 1M
Also consider·Qwen3.5-397B-A17BAlibaba$0.600 / 1M
UsingOpenAI

GPT-4o mini

Recommended→

DeepSeek V4 Flash

DeepSeek

Fast general model with reasoning mode and a 1M-token context window.

Input / 1M
$0.140 / 1M
Output / 1M
$0.280 / 1M
Also consider·DeepSeek V3.2DeepSeek$0.270 / 1M
UsingOpenAI

o1 / o3 (reasoning)

Recommended→

DeepSeek V4 Pro

DeepSeek

Native reasoning mode with strong math, STEM, and multi-step logic. 1M context window.

Input / 1M
$1.69 / 1M
Output / 1M
$3.38 / 1M
Also consider·DeepSeek R1 0528DeepSeek$0.700 / 1M
UsingOpenAI

GPT-5 / GPT-5 mini

Recommended→

DeepSeek V4 Pro

DeepSeek

Frontier-tier general intelligence with 1M context and toggleable reasoning depth.

Input / 1M
$1.69 / 1M
Output / 1M
$3.38 / 1M
Also consider·Qwen3.5-397B-A17BAlibaba$0.600 / 1M
UsingAnthropic

Claude Opus 4.6

Recommended→

Claude Sonnet 4.6

Anthropic

Sonnet 4.6 (also on Thalam) delivers ~98% of Opus performance for most workloads. DeepSeek V4 Pro is the open-weight alternative with comparable reasoning.

Input / 1M
$3.00 / 1M
Output / 1M
$15.00 / 1M
Also consider·DeepSeek V4 ProDeepSeek$1.69 / 1M
UsingAnthropic

Claude Sonnet 4.6

Recommended→

Qwen3.5-397B-A17B

Alibaba

Qwen carries the strongest Arabic support in the catalog. V4 Pro is the open-weight option when Arabic isn’t the priority.

Input / 1M
$0.600 / 1M
Output / 1M
$3.60 / 1M
Also consider·DeepSeek V4 ProDeepSeek$1.69 / 1M
UsingAnthropic

Claude Haiku 4.5

Recommended→

DeepSeek V4 Flash

DeepSeek

Low-latency general model with 1M context. Suited to high-volume customer-facing chat.

Input / 1M
$0.140 / 1M
Output / 1M
$0.280 / 1M
Also consider·DeepSeek V3.2DeepSeek$0.270 / 1M
UsingGoogle

Gemini 3.1 Pro

Recommended→

Qwen3.5-397B-A17B

Alibaba

Long context with strong reasoning. V4 Pro pairs 1M context with native reasoning mode.

Input / 1M
$0.600 / 1M
Output / 1M
$3.60 / 1M
Also consider·DeepSeek V4 ProDeepSeek$1.69 / 1M
UsingGoogle

Gemini 2.5 Pro

Recommended→

Qwen3.5-397B-A17B

Alibaba

Strong reasoning and long context. Qwen leads on Arabic; V4 Pro on agentic coding.

Input / 1M
$0.600 / 1M
Output / 1M
$3.60 / 1M
Also consider·DeepSeek V4 ProDeepSeek$1.69 / 1M
UsingGoogle

Gemini 2.5 Flash

Recommended→

DeepSeek V4 Flash

DeepSeek

Speed-optimised mid-tier with 1M context. Suited to high-throughput production workloads.

Input / 1M
$0.140 / 1M
Output / 1M
$0.280 / 1M
Also consider·DeepSeek V3.2DeepSeek$0.270 / 1M
UsingxAI

Grok 4

Recommended→

DeepSeek V4 Pro

DeepSeek

Reasoning-mode flagship with strong coding and STEM benchmarks. 1M context.

Input / 1M
$1.69 / 1M
Output / 1M
$3.38 / 1M
Also consider·DeepSeek R1 0528DeepSeek$0.700 / 1M
UsingxAI

Grok 4.1 Fast

Recommended→

DeepSeek V4 Flash

DeepSeek

Fast model with a 1M-token context window. Suited to latency-sensitive workloads.

Input / 1M
$0.140 / 1M
Output / 1M
$0.280 / 1M
Also consider·GLM-5Zhipu$1.00 / 1M
UsingMeta

Llama 4 Maverick

Recommended→

Llama 4 Maverick

Meta

Same model — Thalam hosts it directly. Open-weight, multimodal, 1M context.

Input / 1M
$0.270 / 1M
Output / 1M
$0.850 / 1M
UsingMeta

Llama 4 Scout

Recommended→

Llama 4 Scout

Meta

Same model — Thalam hosts it directly. Open-weight, 10M context window.

Input / 1M
$0.180 / 1M
Output / 1M
$0.590 / 1M
UsingMistral

Mistral Medium 3

Recommended→

Mistral Nemo

Mistral

Strong multilingual baseline for high-volume pipelines.

Input / 1M
$0.040 / 1M
Output / 1M
$0.170 / 1M
Also consider·DeepSeek V3.2DeepSeek$0.270 / 1M
UsingMistral

Mistral Small 3.1

Recommended→

Mistral Nemo

Mistral

Lightweight general model with strong multilingual coverage.

Input / 1M
$0.040 / 1M
Output / 1M
$0.170 / 1M
Also consider·GLM-5Zhipu$1.00 / 1M

Two lines. Really.

Change base_url and your API key. Every OpenAI SDK works unchanged.

migrate.py
from openai import OpenAI

client = OpenAI(
    base_url="https://api.thalam.ai/v1",
    api_key="tl-live-...",  # your Thalam key
)

response = client.chat.completions.create(
    model="deepseek/deepseek-v3.2",
    messages=[{"role": "user", "content": "Hello"}],
)

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