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Granite Code 8B vs Claude Haiku 4.5: Which AI Model Should You Choose?

Pricing, context windows, latency, capabilities, and a one-line code switch — everything you need to pick the right model.

Replicate
Code
vs
Anthropic
Multimodal
Verdict

Choose Granite Code 8B for cost-sensitive workloads — it is roughly 20.0× cheaper on input tokens. Choose Claude Haiku 4.5 when you need its broader capabilities or stronger benchmarks.

These models serve different use cases (Code vs Multimodal) — pick the one whose category matches your workload.

Side-by-side specs

SpecGranite Code 8BClaude Haiku 4.5
ProviderReplicateAnthropic
CategoryCodeMultimodal
Input cost / 1M tokens$0.060$1.20
Output cost / 1M tokens$0.30$6.00
Context window128K tokens200K tokens
Max output tokens8,19264,000
Avg. latency——
Featured——
New—Yes
Capabilities
text
text
image

Pricing example

A typical chat workload of 100,000 input tokens plus 50,000 output tokens.

Granite Code 8B
$0.021

100K in × $0.060 + 50K out × $0.30

Claude Haiku 4.5
$0.42

100K in × $1.20 + 50K out × $6.00

For this workload, Granite Code 8B is cheaper than Claude Haiku 4.5 by $0.40 per request.

Switch in one line

Both models live behind Railwail's OpenAI-compatible endpoint. Replace the model string and you are done.

JavaScript / TypeScript
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.RAILWAIL_API_KEY,
  baseURL: "https://railwail.com/v1",
});

// Before — using Granite Code 8B
let r = await client.chat.completions.create({
  model: "ibm-granite/granite-8b-code-instruct-128k",
  messages: [{ role: "user", content: "Hello" }],
});

// After — switched to Claude Haiku 4.5
r = await client.chat.completions.create({
  model: "claude-haiku-4-5-20251001",
  messages: [{ role: "user", content: "Hello" }],
});
Python
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["RAILWAIL_API_KEY"],
    base_url="https://railwail.com/v1",
)

# Before — using Granite Code 8B
r = client.chat.completions.create(
    model="ibm-granite/granite-8b-code-instruct-128k",
    messages=[{"role": "user", "content": "Hello"}],
)

# After — switched to Claude Haiku 4.5
r = client.chat.completions.create(
    model="claude-haiku-4-5-20251001",
    messages=[{"role": "user", "content": "Hello"}],
)
cURL
# Before — using Granite Code 8B
curl https://railwail.com/v1/chat/completions \
  -H "Authorization: Bearer $RAILWAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ibm-granite/granite-8b-code-instruct-128k",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

# After — switched to Claude Haiku 4.5
curl https://railwail.com/v1/chat/completions \
  -H "Authorization: Bearer $RAILWAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-haiku-4-5-20251001",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

Which one wins for...

Quick verdicts derived from public specs. Always validate on your own workload.

Coding
Granite Code 8B

Higher coding category match or larger context wins.

Writing
Claude Haiku 4.5

Bigger context window helps maintain long-form coherence.

Long documents
Claude Haiku 4.5

The larger context window is the deciding factor.

Vision
Claude Haiku 4.5

Multimodal/vision support is required for image inputs.

Real-time chat
Tie

Lower average latency wins for interactive UX.

Cost-sensitive
Granite Code 8B

The model with the lower input-token price wins.

Frequently asked questions

Which is cheaper, Granite Code 8B or Claude Haiku 4.5?
Granite Code 8B is cheaper. On a 100K input + 50K output example, Granite Code 8B costs about $0.021 versus $0.42 for Claude Haiku 4.5 — a saving of $0.40.
Which has more context, Granite Code 8B or Claude Haiku 4.5?
Claude Haiku 4.5 has the larger context window at 200K tokens, compared to 128K tokens for Granite Code 8B.
Is Granite Code 8B better than Claude Haiku 4.5 for coding?
For coding-heavy workloads we lean toward Granite Code 8B on this comparison — it scores higher on the relevant heuristics (category, tags, or context window). Both models are usable for code via Railwail's OpenAI-compatible endpoint, so the safest path is to A/B test on your own prompts.
Can I use both Granite Code 8B and Claude Haiku 4.5 via Railwail?
Yes. Both Granite Code 8B and Claude Haiku 4.5 are accessible through a single Railwail API key and the OpenAI-compatible /v1/chat/completions endpoint. You only change the "model" parameter to switch between them — no SDK swap, no separate billing.
How do I switch from Granite Code 8B to Claude Haiku 4.5?
Replace the model identifier "ibm-granite/granite-8b-code-instruct-128k" with "claude-haiku-4-5-20251001" in your request payload. Everything else — API key, base URL, request shape — stays the same. See the code example on this page for the exact one-line change.

Try Granite Code 8B and Claude Haiku 4.5 side by side

One API key, one endpoint, both models. Start free — no credit card required.

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